Support Document for the Hazardous
Bui®: Rlek Aetesement
for Human snd
Volume I
Sections *\ - 5
Prepared for
U.S. Environmental Proltctfon Agency
Office of Solid Waste
No.
August 1995
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LIST OF TABLES
LIST OF TABLES
Section Page
1-1 Chemicals Included in the Human Analysis ..... 1-9
1-2 Chemicals Included in the Ecological Analysis 1-15
1-3 Sources of Uncertainty in Risk Assessment . . 1-19
1-4 Human Exposure Pathways 1-29
1-5 Ecological Exposure Pathways .... 1-32
1-6 Summary of Human Receptors for Exposure Pathways 1-35
1-7 Summary of Ecological Receptors by Exposure Pathways 1-37
1-8 Pathways Modeled for Each Waste Management Unit . . 1-41
2-1 Roadmap to Equation Sections for Human Pathways 2-8
2-2 Roadmap to Equation Sections for Ecological Pathways 2-15
3-1 Human Receptors, Exposure Routes, and Media of Concern • 3-3
3-2 Ecological Receptors, Exposure Routes, and Media of Concern 3-11
3-3 Results of Data Collection on S
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LIST OF FIGURES
LIST OF FIGURES (continued)
Number ... . • Page
6-7 Fate and transport for beef and milk pathways
involving air and soil . .. . : 6-133
6-8 Fate and transport for beef and milk pathways
involing water 6-134
6-9 Fate and transport for fish pathways 6-206
7-1 Paniculate emissions from ash monofill '..-... 7-21
August 1995 ' X1»
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TABLE OF CONTENTS
TABLE OF CONTENTS
Section Page
List of Figures . . . '. . xii •
List of Tables . . . . xiv
Acknowledgments xix
1,0 Introduction T. 1-1
1.1 Background 1-1
1.2 Why a Multiple Pathway/Receptor Analysis 1-4
1.3 Overview of Multiple Pathway/Receptor Analysis 1-6
1.4 How Uncertainty is Addressed ,. . 1-17
1.5 Linking this Analysis to the Groundwater Fate and Transport 1-^21
1.6 Risk Targets Used . 1 '-21
1.7 How the Analysis is Organized , 1-21
.1.7.1 Receptors 1-21
1.7.1.1 Human Receptor 1-22
1.7.1.2 Ecological Receptors . 1-23
1.7.2 Benchmarks 1-24
1.7.3 Exposure Pathways 1-27
, 1.7.4 Waste Management Units 1-38
1.8 Document Organization .. 1-40
2.0 Guide to Backcalculations I 2-1
2.1 Organization of Document • • • •. 2-1
2.2 Roadmap to Backcalculations •. ..2-7
2.3 Example Backcalculations 2-24
2.34 Human Example Backcalculation 2-25
2.3.1.1 Exposure 2-25
2.3.1.2 Fate and transport > 2-25
2.3.1.3 Waste Management Unit - 2-26
2.3.2 Ecological Example Backcalculation '........;... 2-44 j
2.3.2.1 Exposure . .- • 2-44 ,' I
2.3.2.2 Fate and Transport and Waste Management Unit .... 2-44
3.0 Receptors 3-1 |
3.1 Introduction . .:..... 3-1 j
3.2 Human Receptors !.....:......-..;.. .3-1 |
3.2.1 Conceptual Approach 3-1 !
IQQ< iii
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TABLE OF CONTENTS
TABLE OF CONTENTS (continued)
Section Page
3.2.2 Receptors Exposed to Contaminated Air .................. 3-2
3.2.3 Receptors Exposed to Contaminated Soil 3-2
3.2.4 Receptors Exposed to Contaminated Grouhdwater
and Surface Water .._.'..• ,.. 3.4
3.2.5 Receptors Exposed to Contaminated Plants .. . 3-4
3.2.6 Receptors Exposed to Contaminated Beef and Milk ... ... 3-4
3.2.7 Receptors Exposed to Contaminated Fish 3-4
3.2.8 Uncertainties and Issues of Concern : 3-4
3.3 Ecological Receptors ............:. 3-5
3.3.1 Conceptual Approach 3-5
3.3.2 Receptors in Generic Freshwater Ecosystem 3-10
3.3.3 Receptors in Generic Terrestrial Ecosystem 3-13
. 3.3.3.1 Nonsoil Receptors ... 3-13
3.3.3.2 Soil Community 3-14
3.3.4 Uncertainties and Issues of Concern. 3-21
3.3.4.1 ' Conceptual Approach 3-21
3.3.4.2 Selection of Generic Ecosystems ........'....... 3-21
3.3.4.3 Representing Trophic Elements with Single Species . . 3-22
3.3.4.4 Soil Community Approach .. 3-23
4.0 Benchmarks 4-1
4.1 Introduction 4-1
4.2 Human Health Benchmarks . . .......... 4-2
4.2.1 Conceptual Approach 4-2
4.2.2 Oral Exposures . .. ... 4-2
4.2.3 Inhalation Exposures 4-12
4.2.4 Dermal Exposures — 4-12
4.2.5 Uncertainties and Issues of Concern 4-12
4.2.5.1 Noncarcinogens 4-13
4.2.5.2 Carcinogens ....:.. 4-15
4.3 Ecological Benchmarks 4-15
4.3.1 Conceptual Approacf! . 4-15
4.3.1.1 Overview of Benchmarks for
Ecological Receptors 4-18
4.3.1.2 Constituents of Ecological Concern . 4-19
4.3.1.3 Data Collection . 4-22
4.3.2 Mammals and Birds 4-22
4.3.3 Terrestrial .Plants ............... .. '.... . ..:.... 4-30
4.3.4 Soil Community ...:.... 4-32
4.3.5 Fish and Aquatic Invertebrates . . . ; 4-35
4.3.6 Sediment Community 4-38
A t•/!«•«•• 1OOC
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1
TABLE OF CONTENTS
TABLE OF CONTENTS (continued)
Section Page
4.3.7 Aquatic Plants . . .. 4-41
4.3.8 Uncertainties and Issues of Concern ... 4-44
4.3.8.1. Conceptual Approach 4-44
, 4.3.8.2 Mammals and Birds 4-47
4.3.8.3 Terrestrial Plants, .'...' r 4-49
4.3.8.4 Soil Community 4-50
4.3.8.5 Aquatic Life ....... 4-52
5.0 Exposure. . . . ; 5-1
5.1 Introduction 5-1
5.2 Concentrations for Human Receptors 5-2
5.2.1 Conceptual Approach 5-2
5.2.2 Air Concentrations 5-3
5.2.3 Soil Concentrations 5-16
5.2.3,1 Soil Ingestion 5-16
5.2.3.2 Dermal Soil Exposure ........... 5-26
5.2.4 Surface Water and Groundwater Concentrations 5-55
5.2.4.1 Water Ingestion ...: 5-55
5.2.4.2 Dermal Water Exposure 5-66
5.2.5 Plant Concentrations 5-81
5.2.5.1 Intake .;. .. 5-81
5.2.5.2 Plant Concentration .'...... 5-81
5.2.6 Beef and Milk Concentrations 5-95
5.2.6.1 Intake 5-95
5.2.6.2 Beef or Milk Concentration ... . . 5-95
5.2.7 Fish Concentrations 5-106
5.2.7.1 Intake .5-106
5.2.7.2 Fish Concentration 5-106
5.2.8 Breast Milk Exposure 5-120
5.2.9 Human Exposure Inputs 5-1-24
5.2.9.1 General Exposure Parameters ........' 5-124
5.2.9.2 Intakes/Fraction Contaminated 5-130
5.2.9.3 Miscellaneous Food Chain Parameters 5-135
5.2.9.4 Dermal Exposure Parameters 5-135
5.2.9.5 Breast Milk Exposure Parameters ....... 5-138
5.2.9.6 Chemical-Specific Parameters 5-138
5.2.10 Uncertainty • • 5-146
5.3 Concentrations for Ecological Receptors 5-147
5.3.1 Conceptual Approach 5-147
\5.3.2 Generic Freshwater Ecosystem 1 5-149
• 5.3.2.1 Limnetic Ecosystem 5-157
August 1995
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TABLE OF CONTENTS
TABLE OF CONTENTS (continued)
Section ~ Page
5.3.2.2 Littoral Ecosystem 5-173
5.3.4 Terrestrial Ecosystem . 5-189
5.3.4.1 Plants 5-190
5.3.4.2 Soil Fauna . . 5-190
5.3.4.3 Exposures to Mammals and Birds .: 5-192
5.3.5 Ecological Inputs .:.'....'. . . . . . 5-194
5.3.5.1 Generic Freshwater Ecosystem . . 5-199
5.3.5.2 Generic Terrestrial Ecosystem 5-200
5.3.6 Uncertainties and Issues of Concern ...' 5-202
' 5.3.6.1 Generic Freshwater Ecosystem 5-206
5.3.6.2 Generic Terrestrial Ecosystem , . 5-208
6.6 Fate and Transport Modeling 6-1
6.1 Conceptual Approach — ."-. .... '.'. : 6-1
6.1.1 Pathway Selection .'...• 6-1
6.1.2 Sources of Algorithms 6-7
6.1.3 Backcalculation 6-10
6.1.4 High-End Parameters . -. 6-11
6.2 Air Pathways . '. . . 6-13
6.2.1 Scenarios ...:... 6-13
6.2.1.1 Human .7. 6-13
6.2.1.2 Ecological ....... 1.:.. . . 6-14
6.2.2 Pathway Algorithms . . . 6-14
6.2.2; 1 Pathway 2a: Inhalation (Volatiles) -^ Air
-4 WMU ,...6-15
6.2.2.2 Pathway 2b: Inhalation (Particulates) -> Air
->WMU...... ........'. 6-15
6.3 Soil Pathways ....... ...... 6-16
6.3.1 Scenarios . . . . .'. 6-16
6.3.1.1 Human 6-16
6.11.2 Ecological 6-18
6.3:2 Pathway Algorithms 6-19
6.3.2.1 P?M&ways 3, 5, and Terr I (On Site): Ingestion
(3, Terr lyDermal (5)/Direct Contact (Terr I)
->WMU ' ..6-20
6.3.2.2 Pathways 3, 5, and Terr II (Off Site): Ingestion
(3 and Terr HVDermal (5)/Direct Contact (Terr n) N
. '. -» Overland -> WMU . . .. .' •. . • 6-20
6.3.2.3 Pathways 4, 6, and Terr HI: Ingestion (4 and Terr
III)/Dermal (6)/Direct Contact (Terr IE)-*.
Deposition -» Air -> WMU 6-22
August 1995 • v«
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TABLE OF CONTENTS
TABLE OF CONTENTS (continued)
Section Page
6.3.3 Uncertainty for Soil Pathways 6-34
6.3.3.1 Universal Soil Loss Equation ...:.... 6-34
6.3.3.2 Soil Loss Constant Term 6-34
6.3.3.3 Soil Water Content Equation 6-35
6.4 Groundwater Pathways . ; . . . 6-36
6.4.1 Scenarios . . . .............. 6-36
6.4.1.1 Human . ..' 6-36
6.4.1.2 Ecological 6-37
; 6.4.2 Pathway Algorithms .........' .'.-...•.., 6-37
6.4.2.1 Pathway 1: Ingestion ^ Groundwater -» WMU : . . . 6-37
6.4.2.2 Pathway 14: Dermal (Bathing) -> Groundwater
-> WMU ; .......;... 6-38
6.5 Surface Water Pathways . . . . 6-39
6.5.1 Scenarios 6-39
6.5.1.1 Human 6-39.
6.5.1.2 Ecological 6-40
6.5.2 Pathway Algorithms • • • • 6-43
6.5.2.1 Pathways 17, 37, and Aq I: Ingestion < 17 and
Aq D/Dermal Bathing (37)/Direct Contact (Aq I)
-> Surface Water -> Air -> WMU 6-46
6.5.2.2 Pathways i9, 42, and Aq IE: Ingestion (19 and
Aq IID/Dcrmal Bathing (42)/Direct Contact (Aq HI)
-» Surface Water -» Overland -» WMU 6-57
6.5.2.3 Pathways 20, 38, and Aq IL Ingestion (20 and Aq
ID/Dermal Bathing (38)/Direct Contact (Aq II)
-» Surface Water -» Overland -» Deposition -» Air
^ WMU 6-68
6.5.3 Uncertainty for Surface Water Pathways 6-89
6.5.3.1 Surface Water Modeling Framework 6-89
6.5.3.2 Universal Soil Loss Equation 6-89
6.5.3.3 Soil Loss Constant Term 6-89
. 6.5.3.4 Soil Water Content Equation 6-90
6.6 Food Chain Pathway 6-91
6.6.1 Plant Pathways . . '. 6-91
6.6.1.1 Scenarios .....6-91
6.6.1.2 Pathway Algorithms 6-93
6.6.1.3 Uncertainty 6-130
'6.6.2 Animal Pathways ,. 6-133
6.6.2.1 Scenarios ? 6-133
6.6.2.2 Pathway Algorithms 6-135
6.6,2.3 Uncertainty ••••••• • 6'204
August 1995 , v"
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TABLE OF CONTENTS
TABLE OF CONTENTS (continued)
Section Page
6.6.3 Fish Pathways 6-206
6.6.3.1 Scenarios ....*.. 6-206
6.6.3.2 Pathway Algorithms .. 6-207
6.6.3.3 Uncertainty '.....' 6-266
6.7 Fate and Transport Inputs 6-268
6.7.1 Approach to Selecting Values 6-268
6.7.1.1 Central Tendency vs. High End 6-268
6.7.1.2 Level of Review ! 6-269
. 6.7.2 Meteorological Data 6-270
6.7.2.1 Selection of Locations for Meteorological
Data... 6-270
6.7.2.2 Annual Average Meteorological Data for
Overland Pathways ...........;.. 6-271
6.7.3 SoilData...r .... 6-271
6.7.3.1 Soil Properties 6-27J
6.7.3.2 Soil EJosion Parameters ... 6-275
6.7.3.3 Other Soil-Related Parameters 6-277
6.7.4 Foodchain Data 6-278
6.7.4.1 Plant Parameters . . 6-278
6.7.4.2 Animal (Terrestrial) Parameters .. . 6-283
6.7.4.3 Fish Parameters ... / 6-285
6.7.5 Surface Water Data 6-285
6.7.5.1 Waterbody/Watershed Characterization 6-285
6.7.5.2 Other Surface Water Parameters 6-287
6.7.6 Chemical-Soecific Data ... -..... 6-289
6.7.6.1 Physical-Chemical Properties \ 6-289
6.7.6.2 Biotransfer Factors .. 6-314
6.7.7 Basic Constants ._...- : 6-322
7.0 Waste Management Unit Characterization 7-1
7.1 Conceptual Approach ... 7-1
7.1.1 Types of UgiJs Included ........'....... 7-1
7.1.2 Exclusion of Combustors 7-1
7.1.3 Overview of Land Use .........; 7-2
7.1.4 Approach to Characterization of WMUs ... ...'..• 7-2
7.1.5 Air Modeling . . .. . 7-4
7.1.5.1 Conceptual Approach 7-4
7.1.5.2 Models Used 7-5
7.1.5.3 Types of Outputs 7-7
. 7.1.5.4 Meteorological Data .7-9
7.1.6 Emissions Modeled 7-12
August 1995 * v">
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TABLE OF CONTENTS
TABLE OF CONTENTS (continuedK
Section . . Page
7.1.7 Partitioning . . .. 7-12
7.1.8 Exclusion of Acute and Subchronic Exposures . . . . 7-14
7.2 Ash Monofill . 7-15
7.2.1 - Scenario ..;..... 7-15
7.2 1.1 Land Use and Other Impacted Activities .......... 7-15
7.2.2 Management Unit Characterization :. . . ; 7-15
7.2.3 Waste Characterization 7-19
7.2.4 Emissions Algorithms 7-19
7.2.4.1 Paniculate Emissions 7-20
7.2.5 Air Dispersion Modeling . . . 7-27
7.2.5.1 Air Dispersion Modeling Inputs .-...' . . 7-27
7.2.5.2 Use of Air Dispersion Modeling Results . 7-30
7.2.6 Uncertainty 7-33
7.2.6.1 Particle Size Distribution for Air Dispersion
Modeling 7-33
7.3 Land Application Unit "... 7-3fr
_ 7.3.1 Scenario 7-36
7.3.1.1 Current Land Use and Other Impacted'Activities
(Active Unit) 7-36
7.3.1.2 Future Laini Use and Other Impacted Activities
(Closed Unit) *.-...;, 7-36
7.3.2 Management Unit Characterization 7-37
,^. 7.3.3 Waste Characterization • 7-38
-*^ 7.3.4 Air Modeling , 7-39
7.3.4.1 Air Dispersion Modeling Inputs 7-40
7.3.4.2 Use of Air Dispersion Modeling Results 7-44
7.3.5 Emissions Algorithms 7-48
7.3.5.1 Volatilization 7-48
7.3.52 Paniculate Emissions 7-48
7.3.5.3 Soil Emission/Runoff . 7-50
7.3.5.4 Leaching 7-62
7.3.6 Partitioning^Algorithms 7-62
7.3.7 Uncertainty 7-81
7.3.7.1. Subtitle D Survey 7-81
7.3.7.2 Particle Size Distribution for Air
Dispersion Modeling 7-81
7.4 Wastepile ..: • • • • • 7-84
7.4.1 Scenario 7-84
7.4.1.1 Current Land Use and Other Impacted Activities .... 7-84
7.4.2 Unit Characterization , - • 7'85
7.4.3 Waste Characterization . . . 7-87
August 1995 '*
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TABLE OF CONTENTS
TABLE OF CONTENTS (continued)
Section Page
7.4.4 Air Modeling 7-88
7.4.4.1 Air Dispersion Modeling Inputs , .7-89
7.4.4.2 Use of Air Dispersion Modeling Results ......... 7-93
7.4.5 Emissions Algorithms 7-97
7.4.5.1 Paniculate Emissions 7-97
7.4.5.2 Volatilization 7-102
7.4.5.3 Overland Row/Soil Erosion ; 7-107
7.4.5.4 Leaching : 7-119
7.4.6 Uncertainty 7-119
7.4.6.1 Subtitle D Survey . .. 7-119
7.4.6.2 Wastepile Height 7-119
7.4.6.3 Particle Size Distribution for
Air Dispersion Modeling . '. . 7-120
7.5 Surface Impoundment 7-123
7.5.1 Scenario ...7-123
7.5.1.1 Current Land Use and Other Impacted
Activities 7-123
7.5.2 Unit Characterization • .:.... 7-123
7.5.3 Waste Characterization 7-125
7.5.4 Air Modeling 7-125
7.5.4.1 Air Dispersion Modeling Inputs •'...'. 7-126
7.5.4.2 .Use of Air Dispersion Modeling Results . . 7-126
7.5.5 Emissions Algorithms . 7-130
7.5.5.1 Volatilization .......: 4. 7-130
7.5.5.2. Spill 7-137
7.5.6 Partitioning Algorithms .......... 7-141
7.5.7 Uncertainty '... 7-148
7.5.7.1 Subtitle D Survey . . 7-148
7.6 Aerated Tank 7-150
7.6.1 Scenario 7-150
7.6.1.1 Current Land Use , 7-150
7.6.2 Unit Characterization 7-153
7.6.3 Waste Chaigsjerization 7-153
7.6.4 Air Modeling- . : 7-153
7.6.4.1 Air Dispersion Modeling Inputs 7-155
7.6.4.2 Use of Air Dispersion Modeling Results 7-155
7.6.5 Emissions Algorithms . '. • • 7-157
1 7.6.5.1 Volatilization . . . . • • 7*157
7.7 WMU Modeling Inputs (Other than WMU Characterizations) ... 7-177
7.7.1 Annual Average Meteorological Data 7-177
7.7.2 Soil Properties '... ...... 7-180
i . • . •
August 1995 x
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TABLE OF CONTENTS
TABLE OF CONTENTS (continued)
Section Page
7.7.3 Paniculate Emission Inputs ........... . ..... . ....... 7-182
7.7.3.1 Vegetative Cover ......................... 7-182
7.7.3.2 Threshold Windspeed and f(x) ................. 7-182
7.7.3.3 Particle Size Multipliers ...... . ............. 7-183
7.7.3.4 Roadway Silt Content ...................... 7-183
7.7.3.5 Average Vehicle Speed and
Frequency of Truck Speed Greater than 5.4 m/s .... 7-184
7.7.3.6 Control Efficiency ... ........... . . . ....... 7-184
7.7.3.7 Ash Fraction . ..... ... .......... ......... 7-184
7.7.4 Soil Erosion Inputs ....... ....... ............... . . 7-185
7.7.4.1 Enrichment Ratio . . ........ , . ........ ..... 7-185
7.7.4.2 Area of Receptors: Garden
and Agricultural Field ... ..... . ............ . 7-185
7.7.4.3 Mixing Depth ...... . . ........... ......... 7-186
7.7.4.4 USLE Factors .:..'..' ................ . . . . ____ 7-18$
7.7.4.5 Empirical Intercept Coefficient a ......... , ..... 7-188
7.7.5 Chemical-Specific Inputs ....... ......... .......... 7-188
7.7.5.1 Diffusivity in Air and Water ----- ........ ..... 7-191
7.7.5.2 Henry's Law Constant (H and H*) ............. 7-191
7.7.5.3 Soil-Water Partition
Coefficient (Kj) ..... . .............. ...... 7-192
7.7.5.4 Soil Loss Constant due to Degradation ..... ..... 7-194
7.7.6 Miscellaneous Inputs ........... . ..... . . .......... 7-195
7.7.6.1 Time of Volatilization or Erosion ..... . ........ 7-195
7.7.6.2 Surface Water How ..... . ......... . ........ . 7-195
7.7.6.3 Drop Height. ....... . ....... ..... ......... 7-196
7.7.6.4 Surface Impoundment Spill Parameters .......... 7-197
7.7.7 Basic Constants ..... . ....... . ..... \ ....... ...... 7-197
8.0 Results ......... ....... _____ ..... . , .................. . ---- 8-1
9.0 References ... ............... ....... .............. ..... , ... 9-1
Appendix
A Summary of Physical/Chemical Properties .......... ....... ...... . . A-l
B Toxicological Profiles, for Ecological Receptors . . ----- .... ......... . . B-l
C . Review of Unverified Cancer and Noncancer Health Benchmark
Values for HWIR Constituents ...... ............. ....... ..... ..... C-l
D Central Tendency Results ...... . ..... .... ....... • • • , ..... • • ...... D*1
August 1995 xi
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LIST OF FIGURES
LIST OF FIGURES
Section Page
1-1 General concept of the multiple pathway analysis 1-5
2^1 Human receptors .-..'•..'. v 2-3
2-2 Ecological receptors '. 2-4
2-3 Exposure icons 2-4
2-4 Fate and transport icons 2-5
2-5 WMU icons ... .. . 2-6
2-6 Roadmap to equation sections for air pathways 2-16
2-7 Roadmap to equation sections for soil pathways 2-17
2-8 Roadmap to equation sections for groundwater pathways 2-18
2-9 Roadmap to equation sections for surface water pathways '.. . 2-1*9
2-10 Roadmap to equation sections for plant pathways 2-20
2-11 Roadmap to equation sections for animal pathways involving air and
soil . **....• • • 2-21
2-12 Roadmap to equation sections for animal pathways involving water 2-22
2-13 ' Roadmap to equation sections for fish pathways 2-23
3-1 Framework for selection of ecological receptors . 3-9
3-2 Trophic structure of the soil community ; 3-16
4-1 Data requirements for FCV calculation 4-36
4-2 Illustration of exposure pathways in equilibrium partitioning 4-40
5-1 Schematic of steps needed to calculate protective
exposure concentrations in the freshwater ecosystem 5-148
5-2 Schematic of steps needed to calculate protective
exposure concentrations^ the terrestrial ecosystem 5-150
5-3 Simple limnetic food chain ......... ; 5-158
5-4 Schematic of a five-compartment littoral food-web model 5-.174
6-1 Example of backcalculation vs. forward calculation 6-11
6-2 Fate and transport for air pathways 6-13
6-3 Fate and transport for soil pathways 6-16
6-4 Fate and transport for groundwater pathways . 6-36
6-5 Fate and transport for surface water pathways 6-39
6-6 Fate and transport for plant pathways . 6-91
August 1995 xii
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1.0 INTRODUCTION
SECTION 1.0
INTRODUCTION
1.1 BACKGROUND
The Resource Conservation and Recovery Act (RCRA) directs the U.S. Environmental
Protection Agency (EPA) to develop a national waste management program that promotes the
protection of human health and the environment and conserves valuable material and energy
resources. Wastes to be addressed include hazardous and nohhazardous industrial waste,
special wastes .(e.g., from mining and oil and gas production), and municipal solid waste.
Mismanagement of these diverse wastes presents varying degrees of risk; thus, an effective
waste management program must address a wide variety of wastes, risks, and waste
management practices (57 FR 21450, May 20, 1992).
For more than a decade the Federal Government has defined and implemented the
hazardous waste program under Subtitle C of RCRA. Section 1004(5) of RCRA defines
hazardous waste, in part, as a "solid waste" that may "pose substantial present or potential
hazard to human health and the environment when improperly treated, stored, transported or
disposed of, or otherwise managed." The Agency designates wastes as hazardous in one of
two ways. One way is to identify properties or characteristics of a waste that indicate a
potential hazard if that waste is improperly managed. To date, the Agency has identified four
characteristics—ignitability, conosivity, reactivity, and toxicity (see 55 FR 11796, March 29,
1990 for the expanded toxicity characteristic)—that indicate a potential hazard. Any solid
waste that exhibits any of these characteristics is designated a characteristic hazardous waste
and remains so until it no longer exhibits these characteristics (57 FR 21450, May 20, 1992).
The Agency can also designate wastes as hazardous by "listing." The Agency has
studied wastes generated by 'many industrial activities and has identified several criteria for
determining if a waste is hazardous. For example, a waste may be deemed hazardous if it
• Contains significant levels of^joxic and/or carcinogenic constituents
• Manifests one or more of the hazardous waste characteristics described above
• Has the potential to exert specific detrimental effects on the environment
EPA-has determined that listed wastes typically and frequently contain hazardous constituents
at levels that "pose a substantial or potential threat to human health or the environment if
improperly managed." In general, under its regulations, the Agency has interpreted ''posing a
substantial threat" to mean that the wastes contain toxic constituents many times greater than
August 1995
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1.0 INTRODUCTION
is acceptable for human exposure and that these toxicants are sufficiently mobile and
persistent to reach environmental of human receptors (57 FR 21450, May 20, 1992).
On May 19, 1980 (45 FR 33066), the Agency published final rules governing the
management of hazardous waste. Under the final rules, the definition of hazardous waste
included characteristic hazardous waste, listed hazardous waste, and mixtures of solid wastes
and one or more listed hazardous wastes. The provisions governing mixtures of solid waste
and listed hazardous waste are known collectively as the mixture rule (currently 40 CFR
261.3(a)(2)(iv). This rule requires that a mixture be managed as hazardous unless it has been
delisted (a person may file a petition with EPA to remove a specific waste from the
hazardous waste listing by demonstrating that the waste in question does not pose a hazard
[see 40 CFR 260.22]); In addition, the "derived-from" rule (currently 40 CFR 261.3(c)(2)(i)
and (d)(2)) states that any solid waste generated from the treatment, storage, or disposal of a
listed hazardous waste including any sludge, spill residue, ash, emission control dust or
leachate, remains a hazardous waste unless delisted. Further, 40 CFR 261.3(c)(2)(i) specifies
that any waste (such as rags, clothing, absorbents) that contains a listed waste must be
managed as a hazardous waste ("contained-in rule") These three rules, "derived-from,"
"mixture," and "contained-in," apply regardless of the concentration and mobilities of
constituents in these wastes.
Numerous petitions for judicial review have challenged the May 19, 1980, final rules.
One of these challenges alleged that the definition of hazardous waste proposed on December
18, 1978, did not adequately discuss the "mixture" and "derived-from" rules promulgated in
the final regulations. The question of whetuer the Agency gave adequate notice of the
"mixture" and "derived-from" rules was addressed by a ruling of the Circuit Court of the
District of Columbia on December 6, 1991. The court agreed with the petitioners that the
1978 proposal did not adequately provide notice of either rule and the petitioners did not have
sufficient opportunity to comment (Shell Oil Co. v. EPA, 950 F.2d 751 (D.C Or. 1991)).
The court vacated the rules and remanded them to the Agency because of procedural defects
but did not address any substantive issues raised by the petitioners. The Agency, concerned
about the dangers that may be posed by a discontinuity in the regulation of hazardous waste,
reinstated the rules on an interim basis under section 533(b)(3)(B) of the Administrative
Procedures Act (57 FR 7628, March 3, 1992). . v ,
In addition, the Agency received a rulemaking petition from the Chemical
Manufacturers Association (CMA)^establish concentration-based exemption criteria for the
mixture rule, derived-from rule, and contaminated media rule. CMA submitted, this petition
because it believes these three rules are overly inclusive in that they require hazardous waste
management of mixtures, residues, and contaminated media that contain "innocuous" levels of
hazardous constituents.
On May 20, 1992 (57 FR 21451), the Agency proposed several options for considera-
tion under which regulation of listed hazardous waste under the jurisdiction of RCRA Subtitle
C would cease without the need for a delisting petition. The proposal addressed wastes,
contaminated media, and other materials that, under current rulesi continue to be designated
August 1995 1-2
-------
1.0 INTRODUCTION
as "hazardous waste" (despite treatment and detoxification that reduces constituent
concentrations to4evels of minimum risk). The proposal addressed both the remanded rules
and the CMA rulemaking petitition.
The purpose of the May 20, 1992, proposal was to establish criteria where the regula-
tion of listed hazardous waste under the jurisdiction of RCRA Subtitle C ceases. One of
these approaches proposed consistent and generic risk-based exemption levels for exiting
Subtitle C management The second approach proposed consistent and generic exemption
levels for both entering and exiting Subtitle C management using hazardous waste character-
istics. Additionally, a contingent management system based on the concept that disposal can
modify the actual risk posed by a waste could augment either approach and was proposed as
well.
Under the first approach, the Agency proposed establishing generic exemption levels for
hazardous constituents found in listed waste using a risk-based approach. These
concentration-based exemption criteria (CBEQ represent baseline levels that the Agency
believed were not hazardous and, therefore, wastes with these levels should not be regulated
under the Subtitle C program. These criteria would apply generically to. all wastes regardless
of their origin or eventual manner of disposal. For this approach, the Agency evaluated the -
risk to humans posed by toxic constituents leaching from waste in an unlined landfill to a
groundwater aquifer that is used as a source of drinking water by an individual over a chronic
exposure duration (e.g., 30 years). The proposed risk-based exemption levels were based on
maximum contaminant levels (MCLs) proposed or promulgated under die Safe Drinking
Water Act Otherwise, risk-specific doses (T.3Ds) and reference doses (RfDs) were used for
carcinogens and noncarcinogens, respectively. Listed waste that leaches toxicants at
concentrations lower than the exemption levels would no longer be regulated as hazardous
waste. Constituent leaching levels in waste would be determined using the Toxicity
Characteristic Leaching Procedure (TCLP). The Agency requested comment on two different
approaches for setting CBEC levels: (1) a single multiplier (Le., 100,10, or 1) for all
constituents or (2) an individual multiplier for each constituent The multipliers of 100, 10,
or 1 are often used to represent the dilution and attenuation of the constituent in groundwater.
For example, a multiplier of 100 was used for the constituents in the. 1980 Extraction
Procedure. Derivations of the various multipliers are explained in detail in Section VI of the
proposed rule (57 FR 21450, May 20, 1992). .
The second conceptual approach was based on the current toxicity characteristic
approach for identifying hazardous wastes. This approach would establish the same
characteristic (concentration) threshold for determining whether a waste stream would be
covered as a Subtitle C waste (i.e., entry into the Subtitle C waste system) and when a waste
stream would be exempt from Subtitle C regulation. The current Toxicity Characteristic (TQ
was devised to address the potential adverse health effects of 39 heavy metals and hazardous
organic constituents if they were improperly placed in a landfill. As stated above, the Agency
evaluated the risk in terms of the hazard posed to humans from drinking groundwater
contaminated by toxic constituents leaching from the waste. Under this Expanded
Characteristic Option (ECHO), the Agency would expand the TC from its current list of 39
August 1995 10
-------
1.0 INTRODUCTION
Appendix VTJI hazardous constituents to as many Appendix VHI constituents as possible. As
in the current TGrale, the characteristic level for these new constituents would have been a
multiple of the human "health based limit (HBL), which is derived from an MCL, RfD, or
carcinogenic slope factor. The multiplier would be derived from the EPA Composite Model
for Landfills (EPACML) to reflect the dilution and attenuation of the constituent during
groundwater transport However, as stated above, this multiplier could be a single multiplier
applied generically to all constituents or an individual multiplier for each constituent (57 FR
21450, May 20, 1992).
EPA also presented a contingent management approach under which the ultimate
disposal of a waste may influence the level at which it is exempted under Subtitle C. This
approach could complement either the CBEC or ECHO. The Agency limited its contingent
management option to wastes disposed of in a landfill. Again, the route of exposure would
have been the consumption of groundwater contaminated with leachate from the landfill.
However, the Agency proposed contingent management options that diminish the likelihood
of the occurrence of this route of exposure. Details of this approach are also explained in
Section DC of the proposed rule (57 FR 21450, May 20, 1992).
L2 WHY A MULTIPLE PATHWAY/RECEPTOR ANALYSIS ^
The approach taken in the May 20, 1992, proposed HWIR rule addressed the human
health risk associated with an exposure scenario in which the management of wastes in
unlined landfill units results in the leaching of hazardous constituents into a groundwater
aquifer that is used as a source of drinking water. The RCRA Subtitle C program has
traditionally been based on this scenario. However, given the wide range of physical and
chemical properties of die constituents (e.g., solubility, partitioning coefficients,
bioaccumulation potential), the Agency and commenters on the proposed rule became
concerned that the leachate exposure scenario may not include all important exposure
pathways, and that humans may not be the only receptor important to evaluate. Although the
ingestion of contaminated groundwater by humans may be the appropriate pathway and
receptor to evaluate for some wastes and constituents, it may be underprotective for other
pathways and receptors. In addition, over the past 14 years of implementing the RCRA
program, the Agency has learned more about potential routes of release to the environment
from various management practices. Therefore, the Agency developed a multiple pathway/
receptor analysis that considers the numerous ways that constituents can be released from
various management units to the environment and the fate and transport of each constituent
through the environment to human and ecological receptors. The MPRA is designed to
determine the acceptable concentration in a waste at the waste management unit by
backcalculating from acceptable exposure levels for humans and ecological receptors.
Figure 1-1 illustrates the conceptual approach for the analysis. Depending on the
particular management practice, chemicals are released directly to air, land, or water. Once
released, chemicals will partition into various media depending on their physical and chemical
properties and the characteristics of the environment into which they are released. The
exposure pathways of most concern will depend on these properties and the characteristics of
August 1995 .1-4
-------
1.0 INTRODUCTION
RECEPTORS
^ikt
• Cancor
• Noncancar
Food
Chain
ENDPOINTS
iPMlPlp-:
.
Raproductiv*
Developmental
Contaminated
Madia
AshMonofill
FATE & TRANSPORT
COMPARTMENTS
SOIL*-
sw
I
GW •*•
-»• SEDIMENT
RELEASES FROM
WASTEIK-ANAGEMENT UNITS
Land
Application
Unit
Wastepile
I
Surface
Impoundment
Tank
Figure 1-1. General concept of the multiple pathway analysis.
August 1995
1-5
-------
1.0 INTRODUCTION
^^
the environmental media (e.g., soil, meteorological conditions). For example, some
constituents have- ff-high potential to bioaccumulate in the food chain. Pathways in which
these constituents come in contact with fish, grazing livestock, wildlife, or edible plants
would be important to evaluate. It is likely that highly persistent compounds that tend to
bioaccumulate in the food chain would have lower accceptable waste (in certain waste
management units) for food chain pathways than for the groundwater pathway. As a result,
acceptable waste levels for these constituents based solely on contaminated groundwater
ingestion may present health hazards for human receptors, and possibly to ecological
receptors, exposed through the food chain. Moreover, some constituents are highly unlikely
to exist in groundwater due to their chemical and physical properties or their reactivity with
water, and acceptable waste levels based on groundwater ingestion may be overly restrictive.
In contrast, constituents that are very soluble and tend to move rapidly through the
subsurface, may significantly .impact groundwater or surface water following environmental
release. The analysis undertaken here provides a tool for balancing these different concerns
and attempts to arrive at waste levels that are protective of human health and the environment
yet are not extreme or implausible.
1.3 OVERVIEW OF MULTIPLE PATHWAY/RECEPTOR ANALYSIS
The analysis was developed under, three fundamental premises regarding the objective
of a risk assessment used to support national exit criteria. First, because the multiple
pathway/receptor combinations were evaluated individually (i.e., one at a time), it was critical
that the input parameters that drive each pathway/receptor backcalculation be identified. The
multiple pathway approach can provide sufficiently conservative waste concentrations if the
conditions under which high-end exposures occur can be characterized. Second, evaluating
constituent/receptor/WMU combinations for each exposure pathway should not create
implausible exposure scenarios. Because a number of input parameters used in these models
. are not independent variables, it was particularly important that the correct relationship
between (and among) parameters be incorporated into the modeling. For example, the
volumetric flow rate of a surface waterbody is a function of the watershed area.
Consequently, these parameters were linked in the models so that the physical characteristics
of the waterbody remained consistent with watershed characteristics. Third, although the
Agency acknowledges the potential importance of accidental or catastrophic releases (e.g.,
transportation accidents; spills caused by storm events), the Agency considers these releases
to be of low probability and nonrougne and, therefore, does not believe they are appropriate
for this analysis. As a result, the analysis is focused on low-level exposures from waste
management units that result from existing or expected management practices.
The analysis employs a deterministic approach to evaluate a complicated array of
receptor/pathway/waste management unit (WMU) combinations. The deterministic approach
used for this analysis uses point values in all calculations and produces point estimates of
constituent concentrations for waste for each management unit/exposure pathway/receptor
combination (rather than a distribution). However, in selecting and developing point values
for parameters, all available data were considered. Wherever possible, both a central-
tendency (i.e., approximately 50th percentile) and a high-end (i.e., approximately 90th
-------
1.0 INTRODUCTION
1
percentile) value for. each parameter or parameter group used in the analysis were identified
or estimated. Occasionally, complete parameter distributions were "available. More '
commonly, no description of the parameter distribution was found and only a range or point
value could .be identified. If data were insufficient to delineate a distribution, best
professional judgment was used in determining central tendency and high-end values. Some
parameters, such as molecular weight or the density of water, do not vary, other parameter
values have been set based on. Agency policy for this rulemaking. For example, this analysis
used standard Agency human toxicity benchmarks such as RfDs, reference concentrations
(RfCs), and carcinogenic slope factors contained in the Agency's Integrated Risk Information
System (IRIS) or the Health Effects Assessment Summary fables (HEAST).
The deterministic approach described above (based on identifying critical parameters
and using high-end values only for those parameters) resulted in constituent-specific waste
concentrations that are protective across a broad array of conditions and receptors for multiple
exposure pathways. This approach is consistent with EPA's risk assessment policy and
allows both the Agency and the public to ascertain which parameters drive the calculations
for acceptable waste concentrations. Thus, die deterministic approach tends to create a more
transparent analysis and helps focus data collection and input selection efforts. However, to
be consistent with Agency policy on the characterization of risk, stochastic approaches were
also considered. A stochastic approach such as Monte Carlo analysis has certain advantages
in addressing knowledge, uncertainty by including additional data on the distribution of critical
input parameters. However, after evaluating the models, input data, aniavailable data
distributions, a deterministic approach was determined to be more appropriate for the
development of national exit criteria. In particular, the Agency was concerned that a
stochastic approach might generate exposure scenarios that are physically impossible.
Moreover, the stochastic approach tends to obfuscate the importance of critical parameters in
modeling results and obscure the meaning of modeling results. Because the results are based
on a probability distribution, the stochastic approach would have increased the precision of
the exit criteria without necessarily increasing their accuracy. In short, the complexity of the
multiple pathway analysis strongly suggested that the benefits of a stochastic approach were
far outweighed by the loss of transparency. The deterministic approach, coupled with ah
intensive data collection effort, provided a more appropriate tool to support the risk
assessment of human and ecological receptors.
The analysis relies on models developed in both spreadsheet and database formats that
link the four major components of the analysis together (1) physical, chemical, and biological
properties of constituents of concern^) exposure characterization for human and ecological
receptors; (3) environmental fate and transport of contaminants; and (4) release from the
waste management unit The equations, or constructs, that describe contaminant behavior in.
these four components are arranged to backcalculate from an acceptable exposure level for.a-.
constituent/receptor/pathway/WMU combination to an acceptable waste concentration.
Although these constructs were rewritten as backcalculation algorithms, they were selected.
based on consistency with Agency guidance and appropriateness to the application. For .
example, most of the fate and transport constructs were taken from the Methodology for
Assessing Health Risks Associated with Indirect Exposure to Combustor Emissions (U.S. EI?A,
August 1995 l'7
-------
1.0 INTRODUCTION
1990e; hereafter, the Indirect Exposure document, or IED) as modified by the November 10,
1993, draft of Addendum: Methodology for Assessing Health Risks Associated with Indirect
Exposure to Combustdr Emissions, Working Group Recommendations (U.S. EPA, 1993a;
hereafter, the Addendum).*
The analysis evaluated the 191 chemicals shown in Table 1-1 for human health risks.
These were selected as chemicals commonly occurring in hazardous waste for which human
health benchmarks were available, plus lead. For human receptors, estimates of waste
concentrations were determined for each waste management unit corresponding to a protective
level above the 90th percentile of each of the receptor populations and exposures being
assessed. Central tendency (approximately the 50th percentile) estimates of waste
concentrations were also calculated for each of the receptor populations and exposures being
assessed. "
A subset of 47 of these chemicals, plus silver, was evaluated for ecological receptors
based on a screening analysis conducted to identify constituents likely to present significant
ecological risks at concentrations that are protective of human health. For example,
constituents with Ambient Water Quality Criteria (AWQC) were not necessarily included in
the ecological assessment if it could be demonstrated that health-based water concentrations "
for humans were below the AWQC (i.e., stricter). The subset of 47 chemicals listed in Table
1-2 represents the group of highest priority constituents of ecological concern for which data
were available. For ecological receptors, the approximate percentile level of protection is
difficult to discern. However, it is believed *hat the ecological analysis is conservative with
respect to the overall assessment endpoints (e.g., viability of reproducing populations) given
the way the waste management unit and fate and transport parameters are set, the dietary
habits assumed, and how the toxicity benchmarks are developed. However, the degree of
protection conferred to ecosystems is not known. ;
In summary, the analysis provides a unique tool for backcalculating acceptable waste
concentrations for constituent/pathway/receptor/WMU combinations. However, not all
pathways were evaluated for all receptors and waste management units because the release
mechanisms assumed for each source are generally associated with a limited number of
exposure pathways (i.e., exposure pathways may not be complete). For the 191 constituents,
the analysis matched the environmental transport pathways with releases from waste
management units and human and ecological receptors. The output of the analysis is a range
of waste concentrations for each constituent, reflecting the array of pathway/receptor
combinations considered for each waste management unit The lowest concentration of this
"It should be noted that the Addendum is currently being revised based on comments from the Science
Advisory Board and combined with the IED to generate a single methodology guidance document Therefore.
the equations may change after that revision is completed. If this occurs, the analysis used for this rule-making
will need to be revised. If such a revision is needed and occurs, the Agency will provide an opportunity for
public comment on those changes. " •
August 1995 .1-8
-------
1.0 INTRODUCTION
Title 1-1. Chemicals Included in the Human. Analysis
Chemical Name
Acenaphthent
Acetone
Acetonitrile
Acetophenone
Acrotein
Acrylamide
Acrykxutrile
Aldrin
Allyl chloride
Aniline
Antimony
Arsenic
Barium . . .
Benz(a)anthracene
Benzene
Benzidine
Benzof
-------
1.0 INTRODUCTION
Table 1-1 (continued)
Chemical Name
Chloroaniline. p-
Chlorobenzene
Chlorobenzilate
Chlorodibromomethane
Chloroform
Chlorophenol, 2-
Chromium VI
Chiysene
Copper
Cresol, m-
Cresol. o-
Crcsol.p- i
Cumene
ODD
DDE
DDT
Di-n-butyl phthalate
Di-n-octyl phthalate
Diallate
Dibenz(a,A)anthracene
Dibromo-3-chloropropane, 1,2-
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,4-
Dichlorobenzidine, 33'-
Dichlorodifluoromethane
Dichloroemane, 1,1-
Dichloroethane, 1^-
Dichloroethylene, 1,1-
Dichloroemylene, tis-\2- *
Dichloroethylene, trans-l2~
Dichlorophenol, 2,4- , • •' " •
Dichlorophenoxyacetic add, 2,4- (2,4-D)
Dichloropropane, 1J2-
Dichloropropene. 1.3-
CAS Number
10647-8
108-90-7
510-15-6
124-48-1
67-66-3
95-57-8
7440-47-3
218-01-9
7440-50-8
108-394
95-48-7
10644-5
98-82-8
72-54-8
72-55-9
50-29-3
84-74-2
117-84-0
2303-164
53-70-3
96-12-*
95-50-1
10646-7
91-94-1
75-71-8
75-34-3
1074)6-2
75-354
156-59-2
15640-5
120-83-2
94-75-7
78-87-5
542-75-6
(continued)
August 1995
1-10
-------
1.0 INTRODUCTION
Table M (continued)
Chemical Name
Dichloropropene, or- 1,3-
Dichloropropene, rro/u-1,3-
Dieldrin
Diethyl phthalate
Dietiiylstilbestrol
Dimethoate
Dimethyl phthalate
Dimethylbenz(a)anthracene, 7,12-
Dimethylbenzidine, 3 3*-
Dimemylphenol, 2,4-
Dimemyoxybenzidine, 33'-
Dinitrobenzene. 13-
Dinitrophenoi, 2,4-
Dinitrotoluene, 2,4-
Dinitrotoluene, 2,6-
Dioxane, 1,4-
Diphenylamine
Disulfoton
Endosulfan
Endrin
EpkhlOFohydrin
Ethoxyethanol 2-
Ethyl acetate
Ethyl ether
Ethyl methacrylate
Ethyl methanesuifonate
Ethylbenzene
Ethylene dibnmide
Ethylene thiomea "*&
Fluoranttwne
Huorene
Formaldehyde
Formic acid
Furan
CAS Number
10061-01-5
10061-02-6
60-57-1
84-66-2
56-53-1
60-51-5
131-11-3
57-97-6
119-93-7
105-67-9
119-90-4
99-65-0
51-28-5
. 121-14-2
606-20-2
•123-91-1
122-39-4
298-044
115-29-7
72-20-8
106-89-8
110-80-5
141-78-6
6O-29-7
97-63-2
62-500
1OM1-4
106-93-4
9645-7
206-44-0
86-73-7
50^XW)
64-18-6
110-00-9
(continued)
August 1995
1-11
-------
1.0 INTRODUCTION
Table 1-1 (continued)
Chemical Name
Heptachlor
Heptachlor epoxide
Hexachloro-1.3 -butadiene
Hexachlorobenzene
Hexachlorocyclohexane, a- (a-BHC)
Hexachlorocyclohexane, P- (0-BHQ
Hexachlorocyclohexane, y- (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
Hexachlorophene
Indeno(l^J-c^) pyrene
Isobutyl alcohol
Isophorone
Kepone
Lead
Mercury
Methacrylonitrile
Methanol
Methoxychlor
Methyl bromide (bromomethane)
Methyl chloride (chloromethane)
Methyl ethyl ketone
Methyl isobutyl ketone
Methyl methacrylate
Methyl parathion ., •
Methylcholanthrene, 3-
Methylene bromide
Methylene chloride
Molybdenum **»
^-Nitrosodi-A-propylamine
N-Ifitrosodiphenylamine
W-Nitrosopiperidine
yv-Nitrosopynolidine
Naphthalene
CAS Number
76^44^
1024-57-3
87-68-3
118-74-1
319-84-6
319-85-7
58-89-9
77-47-4
67-72-1
70-304
193-39-5
78-83-1
78-59-1"
143-50-0
7439-92-1
7439-97-6
126-98-7
67-56-1
72-43-5
74-83-9
74-87-3
78-93-3
108-10-I-
80-62-6
298-00-4*
56-*9-5
74^95-3
75-09-2
7439-98-7
621-64-7
86-3O6
100-75-4
930-55-2
91-20-3
(continued)
August 1995
1-12
-------
1.0 INTRODUCTION
Table 1-1 (continued)
«
V .'
Chemical Name
Naphthylamine
Nickel
Nitrobenzene
Nitropropane, 2-
"- Nitrosodi-n-butylamine .
Nitrosodiethylamine
t Nitrosodimethylamine
; r Nitrosomethylethylainine '
Octamethylpyroohosphoramide
'.* Paratiiion
•• „-' Pentachlorobenzene
"" Pentachloronitrobenzene (PCNB)
Pentachlorophcnol
Phenol
;.- Phenyi mercuric acetate
- Phenylenediamine. m- . .
^ Polychlorinated biphenyU
* Pronamide
S" Pyrene
? Pyridine
v Safrde
Selenium ,_
Silver
Strychnine
Stryene
TCDD, 23,7.8-
Tetrachlorobenzene, 1^,4^-
Tetrachloroethane,. 1,1,1,2-
Tetrachloroethane, 1,122-
Tetrachloroethylene
Tetrachlorophenol. 23,4,6-
Tetraethyidithiopyrophosphate
Thallium (I)
CAS Number
91-59-8
7440-02-0
98-95-3
79-46-9
924-16-3
55-18-5
62-75-9 .
10595-95-6
152-16-9
56-38-2
608-93-5
82-68-8
87-86-5
108-95-2
62-38-4
108-45-2
298-02-2
1336-36-3
23950-58-5
129-00-0
110-86-1
94-59-7
7782-49-2
7440-22-4
57-24-9
10042-5
1746-01-6
95-94-3
630-20-6
79-34-5
127-18-4
58-90-2
7440-28-0
(continued)
August 1995r
1-13
-------
1.0 INTRODUCTION
Table 1-1 (continued)
Chemical Name
Toluene
Toluenediamine, 2,4-
Toiuidine, o-
Toluidine, >•
Toxaphene
Trichloro-l^JZ-trifluoroethane, 1,1,2-
Trichlorobenzene, 1.2,4-
Trichloroethane, 1,1,1-
Trichloroetnane, 1.1..2-
Trichloroethylene
Trichlorofluoromethane
Tiichlorophenol, 2,4.5-
TrichlOTOphenol, 2,4,6-
Trichlorophenpxyacetic acid, 2,4 ^-{245-T)
Ti irtilnmnL_lLj-_L_^_ Ljmj-ui^_i^LTji fmmA *) A C fMt«Mw\
I nctuoropnenoxypfopionic acKL, A4 j- (.suvex)
T * hi. L 1^1
inciuoropropane, 1^0-
Trinitrobenzene, rym-
Tris (2^-dibromopropyO phosphate
Vanadium
Vinyl chloride
Xylenes (total)
Zinc
CAS Number
108-88-3
95-80-7
. 95-53-4
10M9-0
8001-35-2
76-13-1
120-82-1
71-55-6
79-00-5
79-01-6
75-69-4
95-95-4
88-06-2
93-76-5
07 "n i
yo-72-1
Of. tO A
ifO-io^ »
99-35-4
126-72-7
7440-62-2
75-01-4
1330-20-7
7440-66-6
August 1995
1-14
-------
i.o INTRODUCTION
' IJiii
Table 1-2. Chemicals Included in the Ecological Analysis
CfreralcatNatae
Acenaphthene
K"*
AUJrin
...... A...........'...»
Afitimoriy
W :^
Di-n-jtttyl phthalate
1 phthalate
yl phthalate
lorocyclohexane. y- (liftdane):
hlorocyclopentadiene
CAS Number
83-32-9
309-00-2
7440-36-0
50-29-3
117-84-0
60-57-1
84-66-2
131-11-3
' 115-29-7
72-20-8
206-44-0
76-44-8
1024-57-3
118-74-1
58-89-9
77^M
70-3CM
143-50-0
7439-92-1
(continued)
' ?.
I
1-15
-------
.1.0 INTRODUCTION
Table 1-2 (continued)
Chemical Name
CAS Number
Mercury
7439-97-6
Methoxychlor
72-43-5
Methyl parathion
298-00-0
Molybdenum
7439-98-7
Nickel
Parathion
744042-0
56-38-2
Pentachlorobenzene
Pentachlorophenol
Polychlorinated biphenyls
608-93-5
87-86-5
1336-36-3
Selenium
7782-49-2
Silver
7440-224
TCDD, 23,7,8-
1746^)1-6
Toxaphene
8001-35-2
Trichlorophenoxyacetic acid, 2,4^-(245-T)
-93-76-5
Vanadium
Zinc
7440^62-2
7440-66-6
«**•
August 1995
1-16
-------
1.0 INTRODUCTION
range (for a particular constituent and WMU) represents the highest exposure
pathway/receptorcombination for that constituent/WMU combination. For example, all
constituents would have been modeled as being released from a surface impoundment to 11
pathways applicable to surface impoundments. For each of these pathways, one or more
receptors were modeled. The lowest waste concentration backcalculated for surface
impoundments for all the pathway/receptor combinations would indicate the most sensitive
pathway and receptor.
The backcalcuiation of acceptable waste concentrations may be broken down into four
basic steps:
Step 1—Specify acceptable risk levels for each constituent. For human receptors, the
Agency used a cancer risk target of 10"6 for carcinogens and a hazard quotient of 1 for
noncarcinogens. For ecological receptors, a hazard quotient of I was used for all
constituents. .
Step 2—Calculate the acceptable concentration in the exposure medium. Using the target
cancer risk level or hazard quotient as a starting point, this analysis backcalculates the
concentration of contaminant in the exposure medium (Le., beef, milk, plant, air, water,
soil) that corresponds to the "acceptable" exposure level The exposure equations
physically describe the receptor (e.g., body weight), how the receptor comes into
contact with the contaminant (e.g., exposure route), how much of the contaminant
medium the receptor inhales or ingests, and the temporal descriptors for exposure (e.g.,
exposure duration). Thus, for a subsistence farmer eating contaminated beef, the
exposure equation specifies the amount of beef eaten on a daily basis, the period of
time over which the contaminated beef is eaten, and the individual's body weight and
lifetime to backcalculate the concentration in the beef.
Step 3—Calculate the concentration at the point of release. Based on the backcalculated
concentration in the exposure medium (from Step 2), the concentration in the medium
to which the contaminant is released to the environment (i.e., air, soil, water) for each
pathway/receptor combination was modeled. The end result of this backcalcuiation is a
medium concentration at the point of release.
Step 4—Calculate the concentration in the waste, this step depends on the characteristics
of the waste managment unit, such as area, cover practices, waste consistency, etc.
• ' **»
1.4 HOW UNCERTAINTY IS ADDRESSED
Uncertainty can be introduced into a risk assessment at every step in die process. It
occurs because risk assessment is a complex process, requiring the integration of
» Release of pollutants into the environment
Augustus 1-17-
-------
1.0 INTRODUCTION
• Fate and transport of pollutants in a variety of different and variable environments by
processes_tbat are often pooily understood or too complex to quantify accurately
• Potential for adverse effects in humans and ecological receptors as extrapolated from
animal bioassays .
• Probability of adverse effects in human and wildlife populations that are highly
variable with respect to chemical sensitivity, physiological status (e.g., age), activity
level, spatial proximity to contaminated areas, dietary habits, and-life style.
Even using the most accurate data with the most sophisticated models, uncertainty is inherent
in the process.
Finkel (1990) classified all uncertainty into four types (parameter uncertainty, model
uncertainty, decision-rule uncertainty, and variability), which are summarized in Table 1-3.
The first two, parameter uncertainty and model uncertainty, are generally recognized by risk
assessors as major sources of uncertainty.
Parameter uncertainty occurs when parameters appearing in equations cannot be
measured precisely and/or accurately either because of equipment limitations or because the
quantity being measured varies spatially or temporally. Random, or sample errors, are a
common source of parameter uncertainty that is especially critical for small sample sizes.
More difficult to recognize are nonrandom or systematic errors that result from bias in
sampling, experimental design, or choice of assumptions.
Model uncertainty is associated with all models used in all phases of a risk assessment
These include the animal models used as surrogates for testing human carcinogenicity, dose-
response models used in extrapolations, as well as the computer models used to predict the
fate and transport of chemicals in the environment The use of rodents as surrogates for
humans introduces uncertainty into the risk factor since there is considerable interspecies
variability in sensitivity. Computer models are simplifications of reality, requiring exclusion
of some variables that influence predictions but cannot be included in models due either to
increased complexity or to a lack of data on that parameter. The risk assessor needs to
consider the importance of excluded variables on a case-by-case basis, because a given
variable may be important in some instances and not in others. A similar problem can occur
when a model that is applicable under average conditions is used for a case where conditions
differ from the average. Finally, choosing the correct model form is often difficult because
conflicting theories seem to explain a phenomenon equally well.
The third type, decision-rule uncertainty, is probably of more concern to risk managers.
This type of uncertainty arises, for example, out of the need to balance different social
concerns when determining an acceptable level of risk. Finkel (1990) provides a complete
discussion of decision-rule uncertainty.
August 1995 1-18
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•1.0 INTRODUCTION
.Table 1-3. Sources of Uncertainty in Risk Assessment*
General Type
Parameter
uncertainty
: • s
Model
uncertainty
Decision-rule
uncertainty
Variability
Specific Source
of Uncertainty
Measurement •
errors
•
Random errors •
•
Systematic errors •
•
•
Surrogate , •
variables
Excluded •
variables
Abnormal •
conditions
Incorrect model •
form
•
•
•
• "**
*
Comments/Examples
Include limitations of equipment, methodology,
and human error
Some processes impossible to measure exactly
Sampling errors
Can be minimized by increasing sample size
Nonrandom errors
Result of inherent flaw in data gathering
processes
Minimize by external peer review
Use of animal bioassays to determine effect on
humans, e.g.
May result from model simplification or failure
to recognize an important variable
Failure to recognize importance of episodic
meteorological events, e.g.
Choice of dose-response model for carcinogens,
e.g. .
More important for risk management, but need
to recognize that value judgments affect choice
of model and interpretation of results
Those important for health.risk assessment
include sources of pollutant releases,
environmental factors, genetic variability, and
lifestyle differences
Even if variability is known (therefore, not in
itself uncertain) it still contributes to overall
uncertainty of the risk assessment
I
I
I
"Adapted from Finkel, 1990.
August 1995
1-19
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1.0 INTRODUCTION
Variability, the fourth source of uncertainty, is often used interchangeably with the term
"uncertainty," but this is not strictly correct Variability may be tied to variations, in physical
and biological processes and cannot be reduced with additional research or information,
though it may be known with greater certainty (e.g., age distribution of a population may be
known and represented by the mean age and its standard deviation). "Uncertainty" is a
description of the imperfection in knowledge of the true value of a particular parameter or its
real variability in an individual or a group. In general, uncertainty is reducible by additional
information-gathering or analysis activities (better data, better models), whereas real
variability will not change (although it may be more accurately known) as a result of better or
more extensive measurements (Hams and Burmaster, 1994).
The degree to which all types of uncertainty need to be quantified and the amount of
uncertainty that is acceptable varies with the intent of the analysis. For a screening level
analysis, a high degree of uncertainty is often acceptable, provided that conservative
assumptions are used to bias potential error toward protecting human health and the
environment A region-wide or nationwide study will be less uncertain than a site-specific
one in determining average risks since, in the former case, it may be possible to use the
average of a parameter value over many sites (which often can be estimated better than a site-
specific value). However, the general analysis may be highly uncertain in defining the range
of possible risks that are influenced by site-specific conditions. In general, the more detailed
or accurate the risk characterization, the more carefully uncertainty needs to be considered.
* »
A detailed description of uncertainty and key issues of concern has been included at the
end of each section of this document and is beyond the scope of this introduction. However,
understanding the overall approach used to address uncertainty in the analysis is crucial,
particularly with respect to parameter uncertainty and variability. For each constituent/
pathway/receptpr/WMU combination, waste concentrations associated with high-end and
central tendency exposures were backcalculated so that: (1) driving parameters could be
identified and die impacts of parameter variability delineated, (2) the sensitivity of the models
could be evaluated across different exposure scenarios, and (3) the range of risk under
different exposure settings could be estimated (e.g., narrow vs. broad risk range).
• The high-end estimates were generated by setting selected input parameters to high-end
values and all other parameters to central tendency values. The two most sensitive
parameters (i.e., driving parameters) were identified for the fate and transport/WMU
components described in Section 1.3 by backcalculating waste concentrations for every
possible combination of two fate an&transport/WMU parameters set at high-end values and
comparing the results for constituent/feceptor/pathway/WMU combinations. The two most
sensitive exposure parameters were identified by mathematical evaluation. For human
receptors, exposure duration and intake were usually set at high-end values. However,
exposure parameters for ecological receptors were hot set at high-end values, per se. Intake
for mammals and birds is a function of body weight and, from an exposure standpoint a
change in one tends to be offset by the resulting change in the other. Nevertheless, the
exposure scenarios for ecological receptors contain a number of assumptions that are
conservative in nature (e.g., 100 percent of the fish consumed by piscivores assumed to
August 1995 • 1-20
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1.0 INTRODUCTION
originate from contaminated stream reach). Having set the two driving exposure parameters
(humans only) and-the two driving fate and transport/WMU parameters at the high end,
constituent concentrations in waste management units were calculated based on a combination
of central tendency and four high-end values.
Central tendency results were obtained by setting all exposure, fate and transport, and
management unit parameters at central tendency values.
1.5 LINKING THIS ANALYSIS TO THE GROUNDWATER FATE AND
TRANSPORT
In this analysis, the pathways involving groundwater are backcalculated from the
receptor to. the wellhead; i.e., the analysis provides constituent concentrations in groundwater
at the well. A separate groundwater fate and transport analysis was used to determine the
concentration of a constituent at the waste management unit from the concentration at the
well.
1.6 RISK TARGETS USED
As previously mentioned, this analysis used existing toxicity benchmarks for human
receptors. However, a number of toxicity benchmarks for ecological receptors were
developed for this analysis. For human health benchmarks, the Agency used a cancer risk
target of 10'6 for carcinogens and a hazard quotient of 1 for noncarcinogens. For ecological
benchmarks, a hazard quotient of 1 was useu ror all constituents.
1.7 HOW THE ANALYSIS IS ORGANIZED
The analysis is organized and presented in terms of four major components: receptors,
benchmarks, exposure pathways, and waste management units. Each of these components is
discussed briefly in the following sections. . '.
1.7.1 Receptors
Both human and ecological receptors are considered in this analysis. The human
receptors evaluated were selected to represent a range of behaviors and activities that
influence exposure levels. These are^bjsh'eved to represent the types of behaviors and
activities that might exist around waste-management units or media contaminated.by releases
. from waste management units. For example, subsistence farming would tend to lead to
higher exposures to lipophilic constituents bioconcentrated in vegetables than would home
gardening. For ecological receptors, populations or communities were selected for the generic
terrestrial and freshwater ecosystems based on behavior patterns such as dietary habits (plant-
eater vs. meat-eater) as well as qualities such as ecological significance and representativeness
with respect to trophic structure in the ecosystem. Ecological receptor selection was
constrained by the availability of information on toxicology, dietary habits, bedy weight, and
habitat
jffiBHHHmjammiBimiftiaff^^
August 1995 ' 1-21.
-------
1.0 INTRODUCTION
1.7.1.1 Human Receptors
Human receptors assessed in this analysis include the following:
Adult resident living in the vicinity of a management unit—This individual is
representive of the general population in the United States and is the typical scenario
used by the Agency for characterizing individual risk. Exposure is based on adults
living in the vicinity of a facility for a substantial portion of their lifetime. Also
included in this analysis is the potential for individuals to live on a closed site. An
adult resident is assessed when examining inhalation, soil ingestion, waste ingestion,
dermal soil exposure, and dermal bathing exposure.
Child resident living in the vicinity of a management unit—Children are a special
population considered in certain exposure pathways because of their low body weight
compared to high intake or surface area. Also included in this analysis is the
potential for children to live on a closed site and thus be exposed via pathways
directly on site. A child is assessed when examining soil ingestion, dermal soil
exposure, and dermal bathing exposure.
Home Gardener—This individual represents a subpopulation that grows fruits and
vegetables to supplement their consumption. Both aboveground fruits and vegetables
and belowground root vegetables are evaluated in this analysis. A home gardener is
assessed when examining fruit and vegetable ingestion.
Subsistence Farmer—This individual represents a subpopulation that grows or raises
most of their own food, including fruits and vegetables, beef cattle, and dairy cattle.
A subsistence farmer is assessed when examining beef ingestion, milk ingestion, and
fruit and vegetable ingestion. For fruit and vegetable ingestion, the subsistence
farmer differs from the home gardener in that the fruits and vegetables grown make
up most of the subsistence farmer's consumption rather than supplementing their
consumption.
Fish Consumer—This individaul represents a subpopulation of individuals who have
an intake of fish comparable to the general population, but who catch their own fish.
A fish consumer is assessed when examining fish ingestion.
"gfct
Subsistence Fisher—This individual represents a subpopulation that subsists on fish
and therefore has a very high intake of fish compared to the general population. A
subsistence fisher is assessed when examining fish ingestion. the subsistence fisher
differs from the fish consumer in the quantity of fish consumed.
Qn-site Worker—This individual represents the working population that may be
found working at waste management units during their active phases. A worker is
assessed when examining on-site inhalation and dermal soil exposure-.
August 1995 1-22
-------
1.0 INTRODUCTION
Mig!ma8Rgacacia~rywxw«?mw:?mm»K?mm^
1.7.1.2 Ecological Receptors
Lacking an Agency precedent for the selection of ecological receptors for a generic
analysis, a simple framework was developed for ecological receptor identification based on
EPA's Framework for Ecological Risk Assessment (U.S. EPA, 1992a). During the problem
formulation phase, a suite of ecological receptors was selected that included species that
represent each of the trophic levels or feeding habits within an ecosystem. By protecting
producers (i.e., plants) and consumers (i.e., predators) at different trophic levels, a degree of
protection from chemical stressors may be inferred to the ecosystems, the species included
in the ecological assessment encompass a wide range of dietary preferences and sizes and, by
virtue of their ecological niche, may be highly exposed to contaminants released to the
environment
In selecting the context for ecological, receptors, a number of ecosystem types were
considered. In their simplest form, ecosystems may be thought of as either aquatic or
terrestrial. Aquatic ecosystems are water-based systems that may include a variety of
waterbodies such as lakes, streams, and estuaries, and terrestrial ecosystems are soil-based
ecosystems such as forest, grasslands, and deserts. Ideally, an ecological risk assessment
should be focused on a specific type of ecosystem (e.g., wetlands) so that the appropriate
ecological receptors are evaluated and the environmental chemistry (e.g., pH of soil) is
characterized. However, ecological risk assessment used in a regulatory setting requires a
somewhat more generic approach when applied to national ecological criteria. Consequently,
two generic ecosystems were considered as the starting point for this ecological risk
assessment a freshwater-based ecosystem aM a terrestrial-based ecosystem. Ecological
receptors were identified at different trophic levels and levels of biological organization based
on: (1) the spatial distribution of chemical stressors in relation to the receptor and (2) the
availability of data with which to assess the risks to that receptor.. The ecological receptor
groups included:
• Mammals—Mammals were evaluated for both generic ecosystems and include upper
trophic level predators (e.g., red fox) and lower trophic level Consumers such as
ruminants (e.g., deer) and insectivores (e:g., shrew). Representative species were
selected to represent a variety of body sizes, habitats, and dietary habits for which
data on body weight, food intake, etc., are available.
• Birds—Birds were also evaluated for both generic ecosystems and include upper
trophic level predators (e.g/?5great blue heron) and lower trophic level consumers that
eat small vertebrates (e.g., hawk), earthworms or large insects (e.g., kestrel), or
vegetation (bobwhite quail). As with the mammals, representative species were
selected to represent a variety of body sizes, habitats, and dietary preferences for
which body weight, etc., are available.
• Plants—•Vascular plants typical of a ge'neric terrestrial ecosystem were evaluated.
Representative species were not chosen due to the general paucity of toxicity data on
plants and the lack of cross-species extrapolation models for plants. The species of
August 1995 -1-23
-------
1.0 INTRODUCTION
plants used to represent plants within the terrestrial ecosystem were determined by
the availability of data and included primarily forage grasses and food crops.
• Soil Community—A number of invertebrate species (e.g., earthworms, nematodes,
insects) and microflora are crucial to the structure and function of the soil
community. Organisms living in or on the soil are exposed through direct contact
(e.g., insects), the ingestion of contaminated soil (e.g., earthworms), and through the
ingestion of other soil dwellers (e.g., centipedes).
• Fish—Fish are important species in the generic freshwater ecosystem and species
from trophic level 3 (e.g., planktiybres, insectivores) and 4 (i.e., piscivores) were
considered. Fish are subject to continuous exposure to contaminated water via gill
exchange and may be highly exposed to bioaccumulative chemicals through the food
chain. .
• Aquatic Invertebrates—Invertebrates are common components of any aquatic
ecosystem. They occupy niches as both predator and prey, and include an extremely
diverse "group" of organisms (e.g., arthropods, molluscs, annelids). The extensive
database on aquatic invertebrates suggests that arthropods (e.g., daphnids) are among
the most sensitive aquatic species (Suter, 1993a). Continuous exposure to
contaminated water was considered the primary route of exposure.
. • ' . * . •
*
• Aquatic Plants—Vascular aquatic plants and algae typical of freshwater aquatic
ecosystems were evaluated. Algal _yecies primarily included green, blue-green, and
diatoms, and data on vascular plants were generally found only for duckweed (e.g.,
Lemna minor, Spriodela polyrhizd). Representative species were not selected due to
the general paucity of data on aquatic plants, particularly vascular plants.
• Benthic Community—The benthic community is composed of a variety of
organisms that are indigenous to most freshwater ecosystems, including organisms
that break down decaying materials (e.g., detrivores) and others that filter organic
materials from the water (e.g., filter feeders). Because these organisms spend most
(if not all) of their lives in the sediment, they are exposed through direct contact and
ingestion of contaminated sediments as well as through the ingestion of other
sediment dwellers.
1.7.2 Benchmarks "**! • •.
This analysis used benchmarks developed from carcinogenic and noncarcinogenic
effects. As previously stated, the primary sources for these benchmarks were the Agency's
Integrated Risk Information System and Health Effects Summary Tables. Additional data
sources were used for several constituents .such as dioxins and furans and polycyclic aromatic
hydrocarbons (PAHs). These human health benchmarks are individual'values with no
quantitative estimates of variability or uncertainty. However, these two sources are
considered by the Agency to be the best sources of human health benchmarks for these
August 1995 llll'm<
-------
1.0 INTRODUCTION
constituents. Human health benchmarks on IRIS have been "verified" through an extensive
EPA workgroup review. Human health benchmarks in HEAST are" considered "unverified"
and.have not been through as extensive a review. All constituents with unverified human
health benchmarks were evaluated for appropriateness for use in this analysis (see Appen-
dix C). Those constituents with values deemed inappropriate were excluded from this
analysis.
For noncarcinogenic effects, the benchmarks are referred to as use oral reference doses
(RfDs) and inhalation reference concentrations (RfCs). An RfD or RfC is an estimate (with
uncertainty spanning perhaps an order of magnitude) of a daily exposure to a constituent for
the human population (including sensitive subgroups) that is likely to be without an
appreciable risk of deleterious effects during a lifetime. It is important to note that
information on exposure levels in the environment (e.g., background levels) are not
considered in the development of an RfD or RfC. Rather, the RfD or RfC reflects the
estimated total permissible daily human exposure from all sources of exposure. RfDs and
RfCs have been calculated for many, but not all, of the noncarcinogenic constituents for
which the Agency is establishing exemption levels.
For carcinogenic effects, this analysis used the oral cancer slope factor and inhalation
cancer unit risk. EPA's Carcinogen Assessment Group (CAG), CRAVE Workgroup, and
ECAO-Cincinnati have estimated the carcinogenic slope factor (CSF) (i.e., the slope of the
"dose-response" curve) and inhalation unit risks for humans exposed to low-dose levels of
carcinogens in the environment The slope factors indicate the upper-bound confidence limit
estimate of excess cancer risk for individuals experiencing a given exposure over a 70-year
lifetime. In practice, a given dose multiplier by the slope factor gives an upper estimate of
the lifetime risk to an individual of developing cancer. By specifying a level of lifetime risk
(no matter how small), one can also estimate the corresponding dose using the slope factor.
Each carcinogen is given a classification of A, Bl, B2, C, D or E. The D and E
classifications indicate that a constituent has ho data on carcinogenic potential or that the data
indicate that the chemical has no carcinogenic potential Therefore, slope factors are available
only for Group A, B, and C carcinogens. The same risk level was used for Group A, B, and
C carcinogens. This approach is consistent with the way carcinogens are treated in the
Toxicity Characteristic rule, hazardous waste listing determinations, and the delisting program.
The rationale for this approach is that, although the classifications indicate the type (human or
animal) and strength of the studies available, which reflects upon the uncertainty about the
carcinogenic potential, the severity of the effect-cancer-warrants equal treatment
^flfft
The ecotoxicological benchmarks were developed for a variety of receptors to reflect
the level of biological organization assessed (e.g., individual, population, community) and the
desire to ensure the viability of wildlife and the ecosystem in which they live. Benchmarks
for ecological receptors were generally established using a no effects level (or no effects
concentration) approach. However, for some receptors, a no effects approach was considered
overly conservative and a lowest effects approach was used. For example, the highly diverse
and nonstandard toxicity data on terrestrial plants make it difficult to determine the ecological
August 1995 , 1-25
-------
1.0 INTRODUCTION
significance of various endpoints and effects levels (Fletcher et aL,.1985). Therefore, an
approach similar-to-the Effects Range Low (ER-L) method developed by the National
Oceanographic and Atmospheric Administration was adopted (Long and Morgan, 1990). The
ER-L is the 10th percentile of the distribution of various toxic effects thresholds for
organisms in sediments. Other approaches to ecotoxicological benchmark development are
discussed briefly below. ,
For populations of mammals and birds, the overall approach used to establish
lexicological benchmarks was similar to the methods used to establish RfJDs for humans as
described in U.S. EPA, 1995b). Each method uses a hierarchy for the selection of toxicity
data (e.g., no effects levels are generally preferred to lowest effects levels) and extrapolates
from a test species to the desired benchmark. However, there are fundamental differences in
the goals of noncancer risk assessments for humans and ecological receptors. Risk
assessments for humans seek to protect the individual while risk assessments for ecological
receptors typically seek to protect populations or communities of important species. The
procedures used to develop benchmarks (i.e., RfDs) for the protection of human health are
very sensitive by design, and go beyond the need to sustain the reproductive fitness in a local
population (U.S. EPA, 1992f). Consequently, benchmarks were developed based on
measurement endpoints from which adverse effects may be inferred at the population level
(e.g., endpoints on reproductive and developmental effects, growth and survival, and
mortality).
• * *
For the terrestrial plant community, lexicological benchmarks were identified from a
summary document on the U.S. EPA databsr; on phytotoxiciry (PHYTOTOX) entitled
lexicological Benchmarks for Screening Potential Contaminants of Concern for Effects on
Terrestrial Plants: 1994 Revision (Will and Suter, 1994). The measurement endpoints were
generally limited to growth and yield parameters because: (1) they are the most common class
of response reported in phytotoxicity studies and, therefore, will allow for benchmark
calculations for a large number of constituents, and (2) they are biologically significant
responses both in terms of plant populations and, by extension, the ability of producers to
support higher trophic levels. For chemicals with a sufficient data set, the benchmark was
established at the 10th percentile for lowest observed effects concentrations (LOECs) that
correspond to approximately a 20 percent effects level
For the aquatic plant community, lexicological benchmarks were identified from a
secondary source, lexicological Benchmarks for Screening Potential Contaminants of
Concern for Effects on Aquatic BiotS* 1994 Revision (Suter and Mabrey, 1994). Unlike the
benchmarks for terrestrial plants; the aquatic plant benchmarks included microphytes (i.e.,
algae) as well as macrophytes (e.g., vascular plants). The measurement endpoints were
similar to those selected for terrestrial plants; however, yield parameters are generally not
relevant to aquatic plants. The endpoints for aquatic plants were selected based on biological
significance and included growth reduction, cell number (for algae only), number of fronds,
and several other effects relevant to the viability of aquatic plant populations.
August 1995 , 1-26
-------
1.0 INTRODUCTION
For populations of fish and aquatic invertebrates, the Final Chronic Value (FCV)
developed for the. AWQC was preferred as the lexicological benchmark. If an FCV was
unavailable and could not be calculated from available data, a Secondary Chronic Value
(SCV) was estimated using methods developed for wildlife criteria estimated for the Great
Lakes Initiative (e.g., 58 FR 20802). The SCV methodology is based on the original species
data set established for the AWQC (Stephan et at, 1985), however, it requires fewer data
points and includes statistically derived uncertainty factors.
For the sediment, community, the approach used to establish lexicological benchmarks
was based on methods presented in the Technical Basis for Deriving Sediment Quality
, Criteria for Nonionic Organic Contaminants for the Protection ofBenthic Organisms by
Using Equilibrium Partitioning (U.S. EPA, 19931). Two key principles form the basis for the
proposed sediment quality criteria (SQQ. First, benthic species, defined as either epibenthic
or infaunal species, have a similar lexicological sensitivity to water column species. As a
result, the toxicity to benthic species can be predicted from data from water-only tests, and, in
particular, FCVs can be used to represent the benthic community. Second, pore water and
sediment carbon are assumed to be in equilibrium and the concentrations are related by a
partition coefficient, K^. This assumption, described as equilibrium partitioning (EqP),
provides the rationale for the equality of water-only and sediment-exposure-effects
concentrations on a pore water basis (U.S. EPA, 19931).
For the soil community, the toxicological benchmarks were based On methods
developed by the Dutch National Institute of Public Health and Environmental Protection
(RTVM). In brief, the RTVM approach estimates a confidence interval containing the
concentration at which the no observed effects concentration (NOEQ for p percent (95th
percentile) of the species within the community is not exceeded. A minimum data set was
established in which key structural and functional components of the soil community (e.g.,
decomposer guilds; grazing guilds) encompassing different sizes of organisms (i.e.,
microfauna, mesofauna, and macrofauna) were represented. As with the AWQC,
measurement endpoints included reproductive effects as well as measures of mortality,
growth, and survivaL
1.73 Exposure Pathways
In selecting environmental fate and transport pathways to include in this analysis,
previous rulemakings and other Agency studies that examine numerous pathways were used
as a guide. For example, the Agenejfchas used such analyses in several recent rules including:
- * •
• Wastes from Wood Surface Protection: Final Rule (59 FR 458, January 4, 1994)
• Standards for Use or Disposal of Sewage Sludge; Final Rules (58 FR 32, February
19, 1993)
/ . ~
• Corrective Action Management Units; Corrective Action Provisions Under Subtitle C:
Final Rule (58 FR 29, February 16,1993).
Augustl!)9S 1-27
-------
1.0 INTRODUCTION
•iinmvm^m^iwwiwv^wvxttmm^^
The sewage sludge and pulp and paper rulemakings, in particular, examined both
human and ecological risk. Other rulemakings under development within, the Office of Solid
Waste also, use multiple pathway risk assessment methodologies including various hazardous
waste listing determinations and the dioxin.emission rules for hazardous waste combustion
units. Most of these;analyses draw on several Agency guidance documents issued in recent
years. In January 1990, the Agency issued an interim report, Methodology for Assessing
Health Risks Associated with Indirect Exposure to Combustor Emissions (U.S. EPA,1990e;
referred to as the/Indirect Exposure Document). This document served as the basis for
further development of multiple pathway analyses by the Agency: in November 1993, the
Agency issued an Addendum to the Indirect Exposure Document and, in April 1994, draft
implementation guidance entitled Implementation Guidance for Conducting Indirect Exposure
Analysis at RCRA Combustion Units. In June 1994, the Agency released a review draft of
Estimating Exposure to Dioxin-Like Compounds: Volumes /-/// (U.S. EPA, 1994a), which
presents an extensive and expanded version of the Agency's previous multiple pathway
exposure analyses. Finally on November 16, 1994, the Agency issued Draft Soil Screening
Guidance (59 FR S922S), which presents a multipathway analysis using air, groundwater, and
soil pathways for soil screening levels at Superfund sites. This analysis draws heavily on the
methodologies presented in these Agency guidance documents to remain consistent with
previous rulemakings.
Based on these previous efforts .by .the Agency, comments by reviewers on previous
draft versions of this analysis, and some screening analyses to determine pathways that are
either very similar or unimportant compared to other pathways, 30 human exposure pathways
and 22 ecological exposure pathways were selected for inclusion in this analysis. These
pathways are presented in matrix form in Tables 1-4 (human) and 1-5 (ecological). In Table
1-4, the first column, exposure media, identifies the medium to which the receptor is exposed.
The second column, route of exposure, identifies the route, such as inhalation or ingestion,
by which a receptor is exposed. The third column, type of fate and transport, classifies the
pathway by the primary mode of fate and transport of the contaminant as described below
(e.g., direct air, groundwater, overland). The fourth column indicates the compartments in the
pathway (e.g., source to air to humans) and whether the exposure scenario is assumed to be
on-site or off-site. This column also includes the pathway number, which serves as a unique
identifier for a particular pathway. Table 1-5 on ecological receptors follows the same order
as Table 1-4. However, because wildlife are so highly coupled to their environment, and
because different receptors are associated with each pathway, the ecological exposure
pathways are organized under eighkgrimary pathways. For example, the Terrestrial
Pathway I (Terr I) is an on-site exposure scenario that includes food chain and direct
exposure to contaminated soil for mammals, birds, plants, and soil fauna. These pathways
correspond to specific exposure pathways evaluated for humans (i.e., pathways 3, 5, 9, 11 a,
and lib).
The pathways examined for human and ecological receptors can be grouped into six
primary modes of fate and transport, as follows:
August 1995 1-28
-------
1.0 INTRODUCTION
1
Table 1-4. Human Exposure Pathways
Exposure
medium
Groundwater
Air
Air
Soil
Soil
Soil
Soil
Soil
Soil
Plant
Route of
exposure
Ingestion
Inhalation
Inhalation
Ingestion
Ingestion
Ingestion
Dermal
Dermal
Dermal
Ingestion
Type of fate and
transport
Groundwater
Direct air
Direct air
Direct soil
Overland
Air deposition
Direct soil
Overland
Air deposition
Air deposition
Pathway1
1 .
WMU -» groundwater -» humans
Ingestion of contaminated groundwater as a drinking water
source.
2a (on site or off site)
WMU -» air -» humans . •
Inhalation of volatiles
2b (on site or off site) . •
WMU -» air -» humans
Inhalation of suspended particulars
3 (on site)
WMU -» humans .
Ingestion of contaminated soil
3 (off site)
WMU -» overland -» humans ,
Ingestion of contaminated soil
__ _.._ ._ _____ „_.
WMU -» air -» deposition to soil -» humans
Ingestion of contaminated soil
5 (on site)
WMU -» humans
Dermal contact with contaminated soil
5 (off site)
«VMU — » overland -* humans
Dermal contact with contaminated soil
6
WMU -» air -» deposition to sur&c« soil -> humans
Dermal contact with contaminated soil
8
(veg/root)
WMU -» air -* deposition to soil/garden crops -+ garden crops
' -» humans
Consumption of contaminated crops grown in home gardens
Plant (veg) -Ingestion
Plant (veg/root) Ingestion
Ak diffusion 8a
WMU -* air -» garden crops -» humans
Consumption of contaminated crops grown in home gardens
Direct soil
9 (on site)
WMU -» garden crops -» humans
Consumption of contaminated crops grown in home gardens
Plant (veg/root) Ingestion
Overland * 9 (off site)
WMU -»overland -» garden crops -» humans
Consumption of contaminated crops grown in home gardens
(continued)
August 1995
1-29
-------
1.0 INTRODUCTION
Table 1-4 (continued)
Exposure
medium
Animal
(beef/milk)
Animal
(beef/milk)
Animal
(beef/milk)
Animal
(beef/milk)
Groundwatcx
Surface water
Route of -
exposure
Digestion .
Ingestion
Ingestion
Ingestion
Dermal
(bathing)
Ingestion
Type of fate and
transport Pathway* .
Air deposition 10
WMU -» air -> deposition to soil/feed crops -» feed crops/soil
— » cattle — » humans
Consumption of animal products with elevated levels of toxicant
caused by eating contaminated feed crops and soil
Air diffusion 10*
WMU -» air -» feed crops -» cattle -» humans
Consumption of animal products with elevated levels of toxicant .
caused by eating contaminated feed crops
Direct soil 11 (on site)
WMU -t feed crops -» cattle -» humans
Consumption of animal products with elevated levels of toxicant
caused 'by eating contaminated feed crops and. soil
Overland 11 (off site)
WMU -» overland -» feed crops/soil -» cattle -» humans
Consumption- of 'animal products with elevated levels of toxicant
caused by eating contaminated feed crops and soil
Groundwater 14
WMU -»• groundwater -» humans
Dermal bathing contact with contaminated groundwater . •
Air diffusion 17 . .
WMU -» air -» surface water — » humans
..igestion of contaminated surface water as a drinking water
source
Surface water Ingestion
Overland
If.
WMU -» overland flow -» surface water -» humans
Ingestion of contaminated surface water as a drinking water
source
Surface water Ingestion
Air deposition
20
surface
WMU -» air -» deposition to soil -» overland' flow
water —* h"f*n*
Ingestion of contaminated surface water as a drinking water
source
Fish
Ingestion Air diffusion 21
WMU -» air -» surface water -» fish -» humans
Consumption offish contaminated by toxicants in surface water
Ingestion Overland -^ 23
WMU -» overland flow .-» surface water -» fish -» humans
Consumption offish contaminated by toxicants in surface water
Ingestion Air deposition 24
• WMU -» air —» deposition to surface soil -» overland flow —»
surface water -» fish -» humans
Consumption offish contaminated by toxicants in surface water
Fish
Fish
(continued)
August 1995
1-30
-------
1
.1.0 INTRODUCTION
Table 1-4 (continued)
Exposure
medium
Animal
(beef/milk)
Animal
(bee&miik)
Animal
(beettnilk)
Surface water
Surface water
Surface water
Route, of
exposure
Ingestion
Ingestion
Ingestion
Dennal
(bathing)
Dennal
(bathing)
Dennal
(bathing)
Type of fate and
transport
Air diffusion
Overland
Air deposition
Air diffusion
Air deposition
Overland
Pathway*
33
WMU -* air -* surface water -» cattle -* humans
Consumption of animal products with elevated levels of toxicant
caused by drinking contaminated surface water
3S
WMU -» overland flow -» surface water -» cattle -» humans
Consumption of animal products with elevated levels of toxicant
caused by drinking contaminated surface water
36 .
WMU -* air -» deposition to soil -» overland flow.-* surface
water -» cattle -» humans
Consumption of animal products with elevated levels of toxicant
caused by drinking contaminated surface water
37
WMU -» air -» surface water. — » humans
Dermal bathing contact with contaminated surface water
3* :
WMU -> air -» deposition to soil -» overland flow -»' surface
water -» humans
Dermal bathing contact with contaminated surface water
42
WMU -» overland flow -> surface water -» humans
Dermal bathing contact with contaminated surface water
Overland = Soil erosion.
Overland flow a Both runoff and soil erosion; or. for surface impoundments, a spill directly to surface water.
Veg « Aboveground fruits and vegetables. ;
Root» Belowground (or root) vegetables. ,
* Some pathway numbers are missing, reflecting pathways that have been eliminated from the analysis or combined with
other pathways. • . •
August 1995
1-31
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1.0 INTRODUCTION
Table 1-5. Ecological Exposure Pathways
Pathway Exposure
Group medium
• Route of Type of fate
exposure and transport Pathway*
Terr I Soil
Ingestion Direct soil
3 (on site)
WMU -> mammals, birds, soil fauna
Ingestion of contaminated soil
Soil Direct Direct soil
contact
Plant Ingestion Direct soil
Soil fauna Ingestion K Direct soil
5 (on site)
WMU -» plants, soil fauna
Direct contact with contaminated soil
T(OB"sS«):~~ :
WMU —» vegetation -» mammals, .birds
Consumption of contaminated vegetation (e.g., forage grasses)
ila(onsite)" """"
WMU -» soil fauna -»' mammals, birds
Consumption of soil fauna (e.g., earthworms, insects) with
elevated levels of toxicant '
Animals Ingestion
Torn Soil Ingestion
Soil Direct
contact
Plant Ingestion
Soil fauna Ingestion
Animals Ingestion
Terr in Soil Ingestion
Soil Direct
contact
Direct soil lib (on site)
WMU -> soil fauna/vegetation -* animals -» predatory
mammals, birds
Consumption of animals with elevated levels of toxicant
Overland 3 (off she) '
WMU — » overland — * mammals, birds, soil fauna
Ingestion of contaminated soil .
Overland 5 (off site)
WMU -» overland -» plants, soil fauna
:-->•• Direct contact with contaminated soil
Overland 9 (off site).
WMU -» overland -» vegetation -» mammals, birds
Consumption of contaminated vegetation (ej*, forage grasses)
Overland lie (off site)
WMU -» overland -» soil fauna -» mammals, birds
Consumption of soil fauna (e.g., earthworms, insects) with
elevated levels of toxicant . •
Overland lid (off site)
WMU — » overland -» soil £uma/vegetation — » animals — »
predatory mammals, birds
Consumption of animals with elevated levels of toxicant
Air deposition 4
WMU -» air -» deposition to soil -4 mammals, birds, soil
fauna . '
**5» . Ingestion of contaminated soil
Air deposition 6 . •
WMU -» air -» deposition to surface soil -» plants, soil fauna
Direct contact witK contaminated soil
Terr IV Plant
Ingestion Air deposition 8
mammals,
WMU -» «ir.-» deposition to soil -* vegetation
birds
Consumption of contaminated vegetation (e^., forage grasses)
(continued)
August 1995
1-32
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1.0 INTRODUCTION
Table 1-5 (continued)
Pathway Exposure
. Group medium
TeirV Plant
Aq I Surface
water
Fish
Surface
water
AqH Surface
water
Fish
Surface
water
Aqm Surface
water
Fish
Surface
water
Route of
exposure
Ingestion
.Ingestion
Ingestion
Direct
contact
Ingestion
Ingestion
, Direct
contact
Ingestion
Ingestion
Direct
contact
Type of fate
and transport
Air diffusion
Air diffusion
Air diffusion
Air diffusion
Air deposition
Air deposition
Air deposition
Overland
Overland
Overland
Pathway*
«a
WMU -» air -» vegetation -» mammals, birds
Consumption of contaminated vegetation (e.g., forage grasses)
17 ;
WMU -» air -» surface water -» mammals, birds
Ingestion of contaminated surface water as a drinking water
source
21
WMU -» air -» surface water -* fish -» mammals, birds, fish
Consumption offish contaminated by toxicants in surface water
37
WMU -» air -4 surface water -» fish, daphnids, benthos
Direct contact with contaminated surface water, sediments
20
WMU -» air -» deposition to soil -» overland flow -» surface
water -» mammals, birds
Ingestion of contaminated surface water as a drinking water
source
24
WMU -» air -» deposition to surface soil -» overland flow -»
surface water — > fish •^ mammals, birds, fish
Consumption offish contaminated by toxicants in surface water
3* . ' , .
WMU -» air -» deposition to soil -» overland flow — * surface
water -» fish, daphnids, benthos
Direct contact with contaminated surface water, sediments
19
WMU — » overland flow — » surface water — » jnammak, birds
Ingestion of contaminated surface water at a drinking water
source
23
WMU -» overland flow -» surface water -+ fish — » mammals,
birds, fish
Consumption offish contaminated by toxicants in surface water
42
WMU -» overland flow -» surface water -» fish, daphnids,
benthos
Direct contact with contaminated surface water, sediments
Overland = Soil erosion. '
Overland flow = Both runoff and soil erosion; or, for surface impoundments, a spill directly to surface water.
• Some pathway numbers are missing, reflecting pathways that have been eliminated from the analysis.
August 1995
1-33
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1.0 INTRODUCTION
• Direct air pathways—Pathways beginning with emissions of volatiles and respirable
(PM10) particulates
• Air deposition pathways—Pathways beginning with air emissions of particulates that
deposit on soil or plant surfaces
• Air diffusion pathways—Pathways beginning with air emissions that, while in the
vapor phase, diffuse directly into surface water or plants
• Groundwater pathways—Pathways beginning with release to groundwater (these are
the pathways that link to the separate groundwater fate and transport analysis)
• ' • Overland pathways—Pathways beginning with overland transport (i.e., surface
runoff and soil erosion) to surface water or transport by soil erosion to off-site fields
• Soil pathways—Pathways involving on-site soil exposures. , . .
Each of the pathways examined has been matched with the relevant human and
ecological receptors. Table 1-6 shows the human receptors evaluated for each pathway, and
Table 1-7 shows the ecological receptors evaluated for each pathway.
. * *
Several types of pathways that were included in earlier versions of this analysis have
been dropped, either because they reflect an implausible scenario, or because available models
were determined to be inadequate. These include:
• Groundwater to surface water pathways—The methodology for groundwater to
surface water pathways was based on the assumption that lateral dispersion of the
contaminant plume in groundwater between the waste management unit and die
groundwater-surface water interface is negligible; however, this may not be the case;
and further research is needed to characterize die amount of lateral dispersion.
• Dermal swimming pathways—These pathways assumed that recreational and fitness
swimming were occurring in a fairly small and swiftly flowing stream, an implausible
scenario. A more plausible scenario for these pathways would use a lake instead of a
flowing water system. However, sensitivity analyses indicate that die dermal bathing
pathways are protective of t!j£ swimming pathways.
* '
• Irrigation pathways—These pathways assumed that forage pastures and home
gardens were being irrigated, an implausible scenario.
In addition, the following exposure pathways were excluded from die MPRA due to
data constraints or based on previous screening analyses:
• Ingestion of water by humans while bathing or swimming—The Digestion rate of
water while swimming or bathing is 30 times smaller than the normal consumption
August 1995 1-34
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1.0 INTRODUCTION
Table 1-6. Summary of Human Receptors for Exposure Pathways
Receptor
Pathway
Suns. Home Subs. Fish
Adult Child farmer gardener fisher consumer Worker
1: Groundwater-ingestion
2a: Direct air-inhalation of volatiles (on site)
2a: Direct air-inhalation of volatiles (off site)
2b: Direct air-inhalation of particles (on site)
2b: Direct air-inhalation of particles (off site)
3: Direct soil-soil ingesdon (on site)
3: Overland-soil ingestion (off site)
4: Air deposition-soil ingesdon
/*•/*
/ V
5: Direct soil-dermal (soil) (on site)
5: Direct soil-dermal (soil) (off site)
6: Air deposition-dermal (soil)
s s
8: Air deposition-veg/root ingestion
8a: Air diffusion-veg/root ingestion
9: Direct soil-veg/root ingestion (on site)
9: Overland-veg/root ingesdon (off site)
10: Air deposition-beef/milk ingestion
10*: Air diffusion-beef/milk ingestion
11: Direct soil-beef/milk ingestion (on site)
11: Overland-beef/railk ingestion (off site)
14: Groundwater-dcrmal (bathing)
17: Air diffusion-drinking, water ingestion
19: Overland-drinking water ingestion
20: Air deposition-drinking water ingestion
21: Air diffusion-fish ingestion
23: Overland-fish ingestion
24: Air deposition-fish ingesdon
33: Air diffusion (SW>bee&milk ingestion
35: Overland (SW)-beef/milk ingestion
(continued)
August 1995
1-35
-------
1.0 INTRODUCTION
Table 1-6 (continued)
Receptor
Subs. Hone Subs. Fish
Pathway Adult Child fanner gardener fisher consumer Worker
36: Air deposition (OF/SW)-bee£/milk /
ingestion
37: Air diffusion (SW>dermal (bathing) / / '
38: Air deposition (OF/SW>dennal (bathing) / . /
42: Overland (SW)-dermal (bathing) V / ' • .
' On-site pathways for recepton other than workers are modeled only for the land applicaatkra unit after closure.
August 1995 1-36
-------
1.0 INTRODUCTION
Table 1-7. Summary of Ecological Receptors by Exposure Pathways
... -
1
Pathway
3: Direct soil-soil ingestion (on site)
3: Direct soil-soil ingestion (off site)
4: Air deposition-soil ingestion
5: Direct soil-dermal soil (on site)
5: Direct soil-dermal 'soil (off site)
6: Air deposition-4ennal soil
Receptor
SoO
Mammals Birds Plants fauna Fish Daphnids Benthos
^ ^ '
/ . / /
/ X /
' ' ^
•/ /
^ . •/ '
8: Air deposition-veg/root ingestion
Sa: Air diffusion-veg ingestion
9: Direct soil-veg/root ingestion (on site)
9: Overland-veg/root ingestion (off site)
lla: Direct soil-soil fauna ingestion (on site)
lib: Direct soil-animals ingestion (on site)
.lie Overland-soil fauna ingestion (off site)
lid: Overland-animals ingestion (off site)
17: Air diffusion-drinking water ingestion
18: Groundwater (SW>drinking water
ingestion
19: Overland-drinking water ingestion
20: Air deposition-drinking water ingestion
21: Air diffusion-fish ingestion
22: Groundwater (SW>fish ingestion
23: Overland-fish ingestion
24; Air deposition-fish ingestion
37: Air diffusion (SW)-direct contact
38: Air deposition (OF/SW)-direct contact
40: Groundwater (SW>direct contact
42: Overland (SW>direct contact
* On-rite pathways are modeled only for the land application unit after closure.
August 1995
1-37
-------
1.0 INTRODUCTION
rate of water used in the drinking water ingesrion pathways; therefore, the drinking
water ingestion pathways should be protective of the incidental water ingestion
pathways. ~ "
. • Inhalation of volatiles by humans while bathing—No appropriate, chemical-
specific equations could be found to address this pathway.
• Ingestion by humans of airborne particulates—The ingestion rate of soil used in
the soil ingestion pathways is many times larger than the ingestion rate from airborne
particulates; therefore, the soil ingestion pathways should be protective of the
ingestion of airborne particulates pathway. Also, given the way the soil ingestion
rates were empirically derived, ingestion of airborne particulates should, in effect, be
accounted for in the soil ingestion pathway.
• Ingestion of water by workers-^—The adult resident would be exposed to the same
groundwater concentration more frequently and for a longer time than the worker,
and so ingestion of water by adult resident is believed to be protective of the worker
for ingestion of water.
• Ingestion of soil by resident on active site—While the waste management units are
active,, it is assumed that access is limited to workers. .
• Inhalation by ecological receptors—No suitable methodology was available.
• Dermal contact with soil—No suitable methodology or sufficient toxicity data were
available.
• Dermal contact with water—No suitable methodology or sufficient toxicity data
were available. ,•''.• '
1.7.4 Waste Management Units
The manner in which constituents are released and the quantity released to each
medium will determine the exposure pathways (and receptors) of most concern for a
particular constituent Identifying the pathway presenting the highest risk to human or
ecological receptors is confounded b$fcthe complex interactions among WMU release
mechanisms, the physical and chemical properties of the constituent, and die environmental
characteristics that influence mobility and persistence. For some constituents, the
management practice will determine which exposure pathway is of most concern. For
example, although benzene tends to migrate to both air and groundwater, the release scenario
for a quiescent surface impoundment suggests that the groundwater ingestion pathway may
pose higher risks than the inhalation pathway. In contrast, the release scenario for aerated
tanks indicates that the air inhalation pathway may pose the higher risks.
August 1995 1-38
-------
1.0 INTRODUCTION
The non-Subtitle C WMUs examined in this analysis includc'units commonly associated
with the management of nonhazardous wastes that are believed to be protective of other, less
common practices. Although municipal landfills can have releases to other media such as air
or overland pathways, the management practices already included in this analysis would result
in higher releases for any constituent/pathway/receptor combination. For example, the release
rate and mass of volatile organics are likely to be higher for aerated tanks than for landfills,
and overland transport of contaminants from a land application unit would naturally be higher
than transport of contaminants buried in a landfill. In addition, a municipal landfill was not
included in the analysis because its principal route of release is being addressed separately
through the groundwater analysis.* .
The waste management units evaluated in this analysis are:
• Aerated treatment tanks—Relative to other types of WMUs, aerated tanks
frequently have the highest releases of volatile organics to air.
• Quiescent surface impoundments—This type of unit also can result in high releases
of volatile organic constituents to air, which may affect groundwater if the unit is not
well maintained or constructed.
• Land application—Active and inactive units can have high releases of certain
constituents to nearby land and surface waterbodies through erosion and runoff. For
active units, high releases of volatile organics to air may occur, depending on tilling
practices. For closed units, high o^ite exposures to persistent and relatively
immobile constituents may present significant risks.
• Ash monoflll—This type of unit can have high releases of particulates to air, which
may be inhaled or deposit on soil and/or plants, resulting in exposure through food
and soil ingestion.
• Wastepiles—This type of unit can have high releases of particulates to air as well as
high releases of particulates through erosion and overland runoff.
One type of WMU was excluded from the analysis based on considerations other than
contaminant releases: waste combustors. The Agency considered different approaches for
including combustors in the analysis and concluded that the products of incomplete
combustion (PICs) created during tn^combustion process could not be predicted for generic
waste streams. Although destruction of hazardous constituents can be predicted based on
* As described earlier, all of the pathways originating from contaminated groundwater are backcalculated in
the analysis to the water well. These pathways will be applicable to all of the WMUs modeled (except tanks);
however, all of the WMU-specific portions of the groundwater fate and transport analysis are contained in a
separate groundwater fate and transport analysis. Because the groundwater results from the analysis are not
directly comparable to the results for other pathways, they are listed in a separate table in the results section of
this document, rather than in the tables of results for each waste management unit.
August 1995 1-39
-------
1.0 INTRODUCTION
certain operating characteristics of combustion units, it is not possible to determine the types
and quantities oLPICs created from combustion for the wide universe of waste matrices (e.g.,
dioxin-like compounds). Although it may be possible to make such predictions for well-
characterized wastes in specific combustion units, the data required to develop, such models
are currently unavailable. Therefore, acceptable constituent levels for wastes managed in
combustors could not be established.*
It should be emphasized that not all pathways were evaluated for all WMUs. By
examining the release mechanisms assumed for each WMU and identifying the pathway types
associated with those release mechanisms, the appropriate pathways to be modeled for each
WMU were identified. The exposure pathways modeled for each WMU are shown in
Table 1-8. ,
1.8 DOCUMENT ORGANIZATION
The rest of this document is organized in eight sections. Section 2 provides an
overview and roadmap to the backcalculations and includes an example backcalculatioh.
Section 3 describes receptors. Section 4 describes the endpoints used to estimate effects on
receptors. Section 5 presents the exposure modeling. Section 6 presents the fate and
transport modeling. Section 7 presents the waste management unit modeling. Section 8
presents the high-end results. Section 9 lists die references.
In addition, several appendices are attached. Appendix A presents chemical-specific
inputs. Appendix 9 presents ecological to?&;ological profiles. Appendix C reviews
unverified health benchmarks. Appendix D presents central tendency results.
*The Agency is developing emission standards for combustion units, anoTthose emission
standards may be a more appropriate vehicle for addressing combustion.
August 1995 . • 1-40
-------
1.0 INTRODUCTION
:-:-:~?~«g^»;a-;l^;^:-wm.:*>;««.m^^
Tahle-1-8. Pathways Modeled for Each Waste Management Unit
Waste management unit
Pathway
Ash
mononil
Land
appL
unit
Surface
WastepUe impound.
Tank
Water
• use
1: Groundwater- ingestion
2a: Direct air-inhalation volatiles
2b: Direct air-inhalation particles S beef7milk ingestion / / / /
' 35: Overland (SW)^jeef/milk ingestion / / S
36: Air deposition (OF/SW)-beef/nulk ingestion / / /
37: Air diffusion (SW>dennal (bathing) / . / S /_
38: Air deposition (OF/SW)^lermal (bathing) / / / .
42:^^iSveriand (SW)-
-------
2.tt GUIDE TO BACKCALCULATIONS 2.1 Organization of Document
SECTION 2.0
GUIDE TO BACKCALCULATIONS
The purpose of this section is to guide the reader through the backcalculations that are
the core of this analysis by describing how this document is organized (Section 2.1), -if
providing a roadmap to the pieces of the backcalculation for each waste management
unit/pathway/receptor combination (Section 2.2), and laying out two example backcalculations
(Section 2.3).
2.1 ORGANIZATION OF DOCUMENT
This document is organized into sections that describe the logic of the analysis and the
details of how the analysis was conducted The goal of the multiple pathway analysis (MPA) ,
is to calculate constituent concentrations in waste that are unlikely to result in adverse effects
oh human health or the environment when .managed as a nonhazardous waste. To arrive at
that constituent concentration, we start with an acceptable exposure concentration or dose at
the receptor and calculate backward through exposure pathways and mechanisms of release to
a concentration in waste that is managed in r particular way. This "backcalculation" can be
presented as follows: . .
receptor •» endpoint evaluated »*• exposure-route/scenario =*
(Section 3) (Section 4) (Section 5)
=» transport through the environment to point of release •»
(Section 6)
=* characterization of waste management unit and release to the environment *>
(Section 7)
*• concentration of constituent in the waste
"*^ (Section 8)
Sections 3, 4, and 5 describe the human and ecological receptors selected for this
analysis, die endpoints for each receptor that were used in the analysis as the basis for the
backcalculation, and the exposure scenario associated with.each receptor, respectively. In
many ways these three chapters together define the scope of the analysis from a policy
standpoint. For example, the policy issues with regard to receptors include the selection of
organisms or populations that are to be protected and special behavioral patterns that may put
specific subpopulations at greater risk than the general population. The inclusion of human
Awust 1995 . 2'1
-------
2.0 GUIDE TO BACKCALCULATIONS 2.1 Organization of Document
and ecological receptors in the analysis provides for broad protection of human health and the
environment However, within each of these groups* specific types of receptors have been
selected that represent populations and subpopulations considered important because of their
behavioral patterns. For example, in the human category, an adult resident represents the
typical receptor included in most human health risk analyses. For this analysis, two
subsistence groups—farmers and fishers—have been included to consider risk to certain
subpopulations that may be at greater risk of exposure because of the foods they consume.
Similarly, workers and children have been included because their behavior can lead to higher
exposures than the general population for specific exposure pathways. The significance of the
exposure pathway depends on the endpoints being evaluated, which are discussed in Section
4. In addition to behavior, the location and duration of exposure affect the level of exposure
as explained in Section 5. We have separated these components into three chapters to better
explain the inclusion of each component in the analysis. In addition, icons are used
throughout these sections to relate these components to the overall anaysis. These icons have
been developed for receptors, routes of exposure, environmental transport pathways, and
sources and are introduced later in this section.
Section 3 provides a description of, and. rationale for, each of the receptors included in
the analysis. The section is divided into two subsections: human receptors and ecological
receptors. Each of these subsections has multiple receptors distinguished by behavioral
patterns and/or location of exposure. Icons have been developed and used throughout the
document to help the reader easily identify the receptors) being discussed. Figures 2-1 and
2-2 show the icons used for human receptors and ecological receptors, respectively.
Section 4 discusses the effects of endpoints used in the analysis for each of the
receptors. For human receptors, the standard cancer and noncancer endpoints are used. For
ecological receptors, a lengthy discussion of the selection of appropriate endpoints is
provided. Because this-is an emerging area of study, an extensive discussion on uncertainty
is presented.
Section 5 presents the exposure scenarios and associated algorithms that are used to
backcalculate from a health or ecological benchmark presented in Section 4 for each receptor
to a media concentration to which a receptor is exposed. Included in this discussion is the
route of exposure, rate and duration of contact, and other standard exposure factors. The
algorithms presented in this section result in a media concentration at the point of exposure
such as a soil concentration, water concentration, air concentration, or food concentration.
These concentrations are inputs to the next set of calculations presented in Section 6. In this
section, the input values for each equation are presented in tabular form. An explanation of
all inputs and their source or derivation is provided. Again* to aid the reader in clearly
identifying the exposure scenario being discussed, a set of icons has been developed (see
Figure 2-3) that present the route of exposure and the media through which exposure is
occurring such as ingestion (Ing) of milk or inhalation (Inh).
Section 6 presents the entire set of equations used to calculate from the concentration of
a contaminant at the point of exposure (i.e., the output of the equations presented in
August 1995 2-2
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1
2.0 GUIDE TO BACKCALCULATIONS
2.1 Organization of Document
^ ^
i
Abult Resident
Home Gardener
Subsistence Farmer
Subsistence Fisher
Worker
Child Resident
Figure 2-1. Human receptors.
Section 5) back through each environmental transport pathway to the concentration at the
point of release. This section is long and complex. All of the equations required for each of
the pathways are presented. This approach has led to considerable redundancy in the section,
although the reader is spared the chore of having to look up cross-referenced equations. The
equations are presented in tables with all their inputs and input values. Accompanying text
discusses the equations used in each pathway. Also discussed in this section ate all sources
of input values for parameters including the derivation of the parameter values when needed.
Uncertainty in the models being used as well as the input values is also discussed. Because
of'the length and complexity of the section, a set of fate and transport icons has been
. developed (see Figure 2-4) and is used throughout the section. Typically, a pathway would
consist of one to three icons.. These icons are intended to help the reader immediately
recognize differences between pathways.
Section 7 presents the equations used to calculate from the concentration of a
contaminant at the point of release tcwhe concentration in waste being managed in a
particular management unit These equations are organized by management practice such as
surface impoundments and land application units. The equations are presented along with all
inputs and input values. For each management practice, a discussion is provided regarding
the equations used and all sources of input values for the management practice parameters
including the derivation of the parameter values when needed. Uncertainty in the models
being used as well as the input values is also discussed. The icons that represent each source
are presented in Figure 2-5.
2-3
-------
2.0 GUIDE TO BACKCALCULATIONS
2.1 Organization of Document
Nbnpredator
Predator
Mammals
Fish
Benthos
Aquatic Plants
V
Nonpradator
Birds
Predator
Daphnids
Soil Fauna
Terrestrial Plants
Figure 2-2, Ecological receptors.
Ing
Milk
Ing
Soil
ing
Root
Inn
Ing
Beef
*
Dermal
Shower
Ing Ing
Veg Fish
Dermal
Soil
Ing
DW
Dermal
Bath
Figure 2-3. Exposure icons.
2-4
-------
2.0 GUIDE TO BACKCALCULATIONS
2.1 Organization of Document
. "V r'*
;•'• . 0 "i Dispersion
** .' • Deposition to soil
JL
Deposition to plants
Diffusion from air
to surface water
JL
<&
Diffusion from air to plant
Plant uptake from soil
Sofl erosion to surface water
Sol erosion to field
Catte grazing
Catte drinking surface water
Rsh bioconcentratiort
from surface water.
Surface water to sediment
Figure 2-4. Fate and transport icons*
Auffust 199S
2-5
-------
2.0 GUIDE TO BACKCALCULATIONS
2.1 Organization of Document
Ash Monofill
Wastepil*
^P^OwMi
land Application Unit
Surfac* Impoundment
I
Tank
Figure 2-5. WMU icons.
Section 8 presents the results of the analysis. Sixteen tables have been generated. For
each of the five waste management units, three tables present results for humans, terrestrial
ecosystems, and aquatic ecosystems. A final table provides groundwater concentrations for
input to the separate groundwater analysis, i he results are organized by constituent and
receptor, starting with the lowest. calculated exemption criterion and its associated pathway.
i •
Several appendixes are provided including the physical and chemical properties for each
constituent and the exotoxicological profiles for each constituent considered for ecological
receptors.
-------
2.0 GUIDE TO BACKCALCULATIONS 22 Roadmap to Backcalculations
2.2 ROADMAP TO BACKCALCULATIONS
Table 2-1 identifies the locations in this document of the equations for each waste
management unit/exposure pathway/receptor combination for human receptors. Table 2-2
provides the same information for ecological receptors. Pathway names are abbreviated; for
complete descriptions of the pathways, see Tables 1-3 and 1-4. In general, all of the
equations in the referenced section are used for the WMU/pathway/receptor combination. An
exception to this is the exposure sections, which will typically contain the equations for all
receptors for a particular type of exposure. For example, inhalation exposure pathways are
modeled for two receptors, an adult resident and a worker. The equations for both of these
receptors are provided in Section 5.2.2.
Figures 2-6 to 2-13 also identify the locations of equations in this document in a
graphical format Each figure shows section references for each pathway component for the
different pathways for each exposure medium, as follows:
• Figure 2-6: Air pathways
• Figure 2-7: Soil pathways
• Figure 2-8: Groundwater pathways
• Figure 2-9: Surface water pathways
. • Figure 2-10: Plant pathways
• Figure 2-11: Animal pathways involving air and soil
• Figure 2-12: Animal pathways involving water
• Figure 2-13: Fish pathways
-------
>
B '
i
i
N>
6°
Table 2-1. .Roadmap to Equation
Waste
management unit Pathway
Ash monorill 2b-Dir air inhal part "
2bw-Dir air inhal part-worker
4-Air dep soil ingest
6-Air dep soil derm adult
6c-Air dep soil denn child
8-Air dep root ingest (F)
8-Air dep root ingest (HG)
g-Air d£ veg ingest (F)
8-Air dep veg ingest (HG)
10-Air dep beef ingest
10-Air dep milk ingest
20-Air dep dw ingest
24-Air dep fish ingest (PC)
24-Air dep fish ingest (SF)
36-Air dep (OF/SW) beef ingest
36-Air dep (OF/S^W) milk ingest
38-Air dep bath denn i adult
38c-Air dep bath deim child
Land app. unit 2a-Dir air inhal vol
2b-Dir air inhal part
2a-Dir (on) air inhal vol
2aw-Dir air inhal vol worker
Receptor
Adult resident
Worker
Resident
Adult resident
Child resident
Subsistence fanner
Home gardener
Subsistence farmer
Home gardener
Subsistence (farmer
Subsistence fanner
Adult resident
Fish consumer
Subsistence fisher
Subsistence farmer
Subsistence farmer
Adult resident
Child resident
Adult resident
Adult resident
Adult resident
Worker
Sections for
Exposure
sectkM
522
522
52.3.1
. 5232
5232
523
523
523
52.5
52.6
52.6
5.2.4.1
• 52.1
52.1
5.2.6
5.2.6
52A2
52.42
522
522
522
522
Human Pathways
Fate & transport
section
6.2.2.2
6.2.2.2
6.3.23
6.3.2.3
6.3.23
6.6.1.2.1 and 6.6.1.2.2
6.6.1 J.I and 6.6.1.2.2
6.6.12.1 and 6.6.12.2
6.6.12.1 and 6.6.12.2
6.622.1 and 6.622.2
6.622.1 and 6.622.2
6.5.2.3
6.6.32.3
6.6.3.2.3
6.62.2.7
6.6.2.2.7 .
6323
6.523
6.22.1
6.2.2.2
622.1
622.1
WMU section
7.2.4.1 and 7.2.5.2
7.2.4.1 and 7.2.5.2
7.2.4.1 and f.2.5.2,
7.2.4.1 and 7.2.5.2
72.4.1 and 7.2.5.2
7.2.4.1 and 7.2.5.2
7.2.4. land 7.2.5.2
7.2.4.1 and 7.2.5.2
7.2.4.1 and 7.2.5.2
7.2.4.1 and 7.2.5.2
7.2.4.1 and 7.2.5.2
7.2.4.1 and 7.2.5.2
7.2.4.1 and 7.2.5.2
7.2.4.1 and 7.2.5.2 -
7.2.4.1 and 7.2.5.2
7.2.4.1 and 7.2.52
7.2.4.1 and 7.2 32
7.2.4.1 and 7.2.5*2
73.4.2. 73.5.1. and 7.3.
-------
Waste •
management unit Pathway
Land app. unit 2b-Dir (on) air inhal part
2bw-Dir air inhal part worker .
3-Dir (off) soil ingest
3-Dir (on) soil ingest
4-Air dep soil ingest
5-Dir (off) soil derm adult
• 5c-Dir (off) soil derm child
5-Dir (on\ soil derm adult
5^u (on) soil derm chilid
5w-Dir (on) soil derm worker
6-Air dep soil derm adult
6c-Air dep soil demt child
8-Air dep root ingest (F)
ft-Air dep root ingest (HG)
ft-Air dep veg ingest (F)
ft-Air dep veg ingest (HG)
9- Dir (off) root ingest (HG)
9-Dir (off) root ingest (F)
9-Dir (on) root ingest (F)
9-Dir (on) root ingest (HG)
9- Dir (off) veg ingest (HG)
9-Dir (off) veg ingest (F)
Table 2-1
Receptor
Adult resident
Worker
Resident
Resident
Resident
Adult resident
Child resident
Adult resident
Child resident
Worker
Adult resident •
Child resident
Subsistence fanner
Home gardener
Subsistence fanner
Home gardener
Home gardener
Subsistence farmer
Subsistence fanner
Home gardener
Home gardener
Subsistence fanner
(continued)
Exposure
section
522
522
523.1
523.1
523.1
5232
5232
5232
5232
5232
5232
5.2.3.2
52.5
52.5
525
52.5
52.5
52.5
52.5
52.5
52.5
52J5
Fate & transport
section
6222
6222
6322
632.1
6323
6322
6322
632.1
632.1
632.1
6323
6323
6.6. 1.2. land 6.6. 1.2.2
6.6.1.2.1 and 6.6.1.2.2
6.6.1.2.1 and 6 A 1.2. 2
6.6.1.2.1 and 6.6.122
6.6.1.2.4
6.6.12.4
6.6.12.3
6.6.1.2.3
66.12.4
6.6.12.4
WMU section
7.3.4.2. 7.3.5.2. and 7.3.6
7.3.4.2. 7.3.52. and 7.3.6
7.3.5.3 and 7.3.6 ,
7.3.5.3 and 7.3.6
7.3.42. 7.3.5.2. and 7.3.6
7.3.5.3 and 7.3.6
7.3.5.3 and 7.3.6
7.3.5.3 and 7.3.6
7.3.5.3 and 7.3.6
7.3.5.3 and 7.3.6
7.3.4.2. 7.3.5.2. and 7.3.6
7.3.4.2. 7.3.5.2. and 7.3.6
7.3.4.2. 7.3.52. and 7.3.6
7.3.4.2. 7.3.5.2. and 7.3.6
7.3.4.2. 7.3.5.2. and 7.3.6
7.3.42. 7.3.52. and 7.3.6
7.3.5.3 and 7.3.6
7.3.5.3 and 7.3.6 '
7.3.5.3 and 7.3.6
7.3.5.3 and 7.3.6
7.3.5.3 and 7.3.6
7 J.5.3 and 7.3.6
(continued)
b
b
O
m
3
69
O
0
r*
>
O
M
K»
i.
90
0
•o
o
CO
1
calculations
-------
.-
Waste
management unit Pathway
Land app. unit 9-Dir (on) veg ingest (F)
9-Dir (on) veg ingest (HO)
10-Air dep beef ingest
10-Air dep milk ingest
. Il-Dir (off) beef ingest
Il-Dir (on) beef ingest-
1 1-Dir (off) milk ingest
Il-Dir (on) jnilk ingest
17-Air diff dw ingest
19-Ovl dw ingest
20-Air dep dw ingest
21-Air diff fish ingest (FC)
21-Air diff fish ingest (SF)
23-Ovl fish ingest (FC)
23-Ovl fish ingest (SF)
24-Air dep fish ingest (FC)
24-Air dep fish ingest (SF)
33-Air diff beef ingest
33-Air diff milk ingest
35-Ovl(SW) beef ingest ' .
35-Ovl (SW) milk ingest
36-Air dep (OF/SW) beef ingest
Table 2-1
Receptor
Subsistence fanner
Home gardener
Subsistence farmer
Subsistence fa*mw
Subsistence fanner
Subsistence farmtr
Subsistence fanner
Subsistence fanner
Adult resident
Adult resident
Adult resident •»
Fish consumer
Subsistence fisher
Fish consumer
Subsistence fisher
Fish consumer
Subsistence fisher
Subsistence farmer
• "
Subsistence fanner
Subsistence fanner
Subsistence farmer
Subsistence fanner
(continued)
Exposure
section
52.5
52.5
5.2.6
52.6
52.6
526
526
526
52.4.1
52.4.1
5.2.4.1
52.7
52.7
52.7
527
52.7
52.7
526
52.6
52.6
52.6
52.6
Fate & transport '
„ section
6.6.12.3
6.6.123
6.6.2.2.1 and 6.6.2.2.2
6.62.2.1 and 6.622.2
6.622.4.
6.6223
6.622.4
6.622.3
6.52.1
6.522
6.523
6.632.1
6.632.1
6.6322
66322
6.6323
6.6.32.3
6622.5
6622.5
66226
6.622.6
6.622.7
•
WMU section
7.3.5.3 and 7.3.6
7.3.5.3 and 7.3.6
7.3.42, 7.3.5.2., and 7.3.6
73.4.2. 73.5.2. and 73.6
73 33 and 7.3.6
73.53 and 7.3.6
73.5.3 and 73.6
73.53 and 7.3.6
73.42. 7.3.5.1. and 7.3.6
73.5.3 and 7.3.6
73.42. 7.3.5.2, and 7.3.6
73.4.2. 7.3.5.1. and 7.3.6
73.4.2. 7.3.5.1. and 7.3.6
73.53 and 7.3.6
7 3 S "1 and 7 -i (.
73.42. 73 32. and 7.3.6
73.42. 73.52. and 7.3.6
7 3"4_1 7 1 < 1 aiul 7 "fH
73.42. 7.3.5.1. and 7 ^.3.6
7.3.53 and 73.6
73.53 and 7.3.6
73.4.2, 73.52. and 7.3.6
(continued)
K»
b
O
£
o
n
O
00
^
n
9?
n
•^
£
n
g
d'
o
V)
1st
t .
5H
0
M
•a
e
OB
1
calculations
-------
August
i
I
"
Waste
management unit Pathway
Land app. unit 36-Air dep (OF/SW) milk ingest
37-Air dift balb dam adult
37c-Air diff path derm child
38-Air dq> bath dam adult
38c-Air dep bath deim child
42-Ovl (SW) bath dam adult
42c-Ovl bath dam child
Wastepile 2a-Dir (offib air inhal vd
2b^Dir (off) air initial part
2aw-Dir (on) air inhal vol worker"
2bw-Dir (on) air inhal part-worker
3-Dir (off) soil ingest
4-Air dep soil ingest
S-Dir (off) soil derm adult
Sc-Dir (off) soil derm child
5-Dir (on) soil derm worker
6-Air dep soil derm adult
6c-Air dep soil derm child
8-Air dep root ingest (F)
8-Air dep root ingest (HG)
8-Air dep veg ingest (F)
8-Air dep veg ingest (HO)
. • •
Table 2-1
Receptor
Subsistence farmer
Adult resident
Child resident
Adult resident
Child resident
Adult resident
Child resident
Adult resident
Adult resident
Worker
Worker
Resident
Resident
Adult resident
Child resident
Worker
Adult resident
Child resident
Subsistence farmer
Home gardener
Subsistence farmer
Homegardena
(continued)
Exposure
sectkM
3.2.6
5.2.4.2
5.2.4.2
5.2.4.2
52.42
52.42
52.42
522
522
522
522
52.3.1
523.1
5232
5232
5232
5232
5232
523
523
52.5
52.5
Fate & transport
scctioa
6.6.2.2.7
6.52.1
6.5.2.1
632 3
6323
6322
6322
622.1
6222
622.1
6222
6322
6323
6322
6322
632.1
6323
6323
6.6.12.1 and 6.6.1.2.2
6.6.12.1 and 6.6.1.2.2
6.6.1.2.1 and 6.6. 1.2. 2
6.6.1 J2.I and 6.6.1.2.2
WMU section
7.3.4.2. 7.3.5.2. and 7.3.6
7.3.4.2. 7.3.5.1. and 7.3.6
73.4.2, 7.3.5. Land 7.3j6
7.3.4.2. 7.3.5.2. and 7.3.6
7 .3.4.2. 7.3.5.2. and 7.3.6
7.3 33 and 7.3.6
7.3.5.3 and 7.3.6
7.4.4.2 and 7.4.5.2
7.4.4.2 and 7.4.5.1
7.4.4.2 and 7.4.5.2
7.4.42 and 7.4.5.1
7.4.5.3
7.4.42 and 7.4.5.1
7.4.5.3
7.4.5.3
7.4.5.3
7.4.4.2 and 7.4.5.1
7.4.4.2 and 7.4.5.1 '
7.4.4.2 and 7.4.5.1
7.4.4.2 and 7.4.5.1
7.4.4.2 and 7.4.5.1
7.4.42 and 7.4.5.1
(continued)
b
O
&
o
PI
3
60
j>.
n
K
n
£
^j
s
U5
K
ks>
9O
0
O.
3
•o
o
00
K
calculations
-------
;
Waste
management unit Pathway
Wastepile 8a-Air diff veg inge* (F)
8a-Air diff veg ingest (HG)
9-Dir (of 0 root ingest (F)
9-Dir (off) root ingest (HG)
9-Dir (off) veg ingest (F)/
9-Dir (of 0 veg ingest (HO)
10-Air dep beef ingest
10-Dir air bctf ingest
10-Air dep milk ingest
10-Dir air milk ingest
1 1-Dir (off) beef ingest
Il-Dir (off) milk ingest
17-Air diff dw ingest
19-Ovl dw ingest
20-Air dep dw ingest
21-Air diff fish ingest (PC) .
21-Air diff fish ingest (SF)
23-Ovl fish ingest (PC)
23-Ovl fish ingest (SF)
24-Air dep fish ingest (PC)
1 24-Air dep fish ingest (SF)
33-Ak diff (OF/SW) beef ingest
Table 2-1
Receptor
Subsistence farmer
Home gardener
Subsistence farmer
Home gardener
Subsistence farme*
Home gardener
Subsistence farmer
Subsistence fanner
Subsistence farmer
Subsistence farmer
Subsistence fanner
Subsistence farmer
Adult resident '
Adult resident
Adult resident
Fish consumer
Subsistence fisher
Fish consumer
Subsistence fisher
Fish consumer
Subsistence fisher
Subsistence fanner
(continued)
Exposure
section
523
523
523
523
523
523
52.6
52.6
5216
52.6
5.2.6
52.6
52.4.1
5.2.4.1
52.4.1
52.7
5.2.7
52.7
52.7
*
52.7
52.7
52.6
Fate & transport
6.6.12.1 and 6.6.12.2
6.6.12.1 and 6.6.12.2
6.6.12.4
6.6.12.4
6.6.12.4
6.6.12.4
6.6.22. 1 and 6.6222
6.622. land 6.622.2
6.622.1 and 6.62.22
6.622.1 and 6.622.2
6.622.4
6.622.4
6.52.1
6.522
6.523
6.632.1
6.632.1
6.632.2
6.632.2
6.6323
6.6323
6.622.5. .
WMU section
7.4.42 and 7.4.5.2
7.4.42 and 714.52
7.4.5.3 } ,
7.4.5.3
7.4.53
7.4.5.3
7.4.42 and 7.4.5.1
7.4.42 and 7.4.52
7.4.42 and 7.4.5.1
7.4.4.2 and 7.4.52
7.4.5.3
7.4.5.3
7.4.42 and 7.4.52
7.4.53
7.4.4.2 and 7.4.5.1
7.4.42 and 7.4.52
7.4.42 and 7.4.52
7.4.53
7.4.53
7.4.42 and 7.4 J.I
7.4.42 and 7.4.5.1
7.4.42 and 7.4.52
(continued)
Kt
O
o
m
H
O
CO
n
o
r-
^
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0
CO
K»
L.
90
O
CL
§
•o
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00
1
calculations
-------
*
•
•
i
n
N)
l>-
Waste
management unit Pathway
Wastepile 33-Air diff (OF/SW) milk ingest
3 5-Ovl
O
93
n
j.
R
?
H
s
in
M
t.
yo
0
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•o
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00
8
. *r
calculations
-------
^
3
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I
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i-j
jl
Waste
management unit Pathway
Surface 23-Ovl fish ingest (SF)
impoundment 33-Air diff beef ingest
33-Air diff milk ingest
35-Ovl(SW) beef ingest
35-Ovl (SW) milk ingest
37-Air diff bath demaduk
37c-Ak d|ff bath denn child
42-Ovl (SW) bath denn adult
42c-Ovl (SW) bath derm child
Aerated tank ia^Dir air inhal vci
2aw-Dir air inhal vol worker
8a-Air diff veg ingest (F)
8a-Air diff veg ingest (HG)
lOa-Dir air beef ingest
lOa-Dir air milk ingest
17-Air diff dw ingest
21-Air diff fish ingest (FC)
21-Air diff fish ingest (SF)
33-Air diff (OF/SW) beef ingest
33-Air diff (OF/SW) milk ingest
37- Au diff bath denn adub
37c-Air diff bath derm child
-
Table 2-1
Receptor
Subsistence fisher
Subsistence fanner
Subsistence fanner
Subsistence fanner
Subsistence fanner
Adult resident
Child resident
Adult resident
Child resident
Adult resident
Worker '
Subsistence fanner
Home gardener
Subsistence fanner
Subsistence fanner
Adult resident
Fish consumer
Subsistence fisher
Subsistence farmer
Subsistence farmer
Adult resident
Child resident
(continued)
Exposure
section
5.2.7
52.6
52.6
52.6
52.6
52.42
52.42
52.42
52.42
522
522
52.5
52.5
5.2.6
5.2.6
52.4.1
52.7
52.7
52.6
*
52.6
5.2.4.2
52.4.2
Fate & transport
section
6.6.322
6.622.5
6.622.5
6.622.6
6.6.22.6
652.1
6.52.1
6'.522
6522
622.1
622.1
6.6.1.2.1 and 6.6.1.2.2
6.6.12.1 and 6.6.12.2
6.6.2.2.1 and 6.6222
6.6.2.2.1 and 6.6.222
6.52.1
6.6.32.1
6632.1
6.622.5
6.622.5
6.52.1
6.52.1
WMU section
7.5.52 and 7.5.6
7.5.4.2. 7.5.5.1. land 7.5.6
7J.4.2. 7.5.5.1, 'and 7.5,6
7.5.52 and 7.5.6
7.5 .52 and 7.5.6
7J.42. 7.5.5.1. and 7.5.6
7.5.42. 7.5.5.1. and 7.5.6
7.5.52 and 7:5.6
7^ 62 and 7.5.6
7.6.42 and 7.6.5.1
7.6-42 and 7.6.5.1
7.6.42 and 7.6.5 1
7.6.4.2 and 7.6.5.1
7.6.42 and 7.6.5.1
7.6.4.2 and 7.6.5.1
7.6.4.2 and 7.6.5.1
7.6.42 and 7.6.5.1
764 2 anil 7 A S 1'
7.6.42 and 7.6.5.1
7.6.42 and 7.6.5.1
7.6.42 and 7.6.5.1
7.6.42 and 7.6.5.1
Kt
b
O
^i
tJ
Pi
H
O
00
n
x
n
•»»
**
$
k-j
o
^
' t*
»»» .
o
•> •
o.
•o
'0
00
n
calculations
-------
!
»•
I
Table 2-2. Roadmap (o Equation Sections for Ecological Pathways
K>
Cn
Waste
Management Unit
Ashmonofill
Land app. unit
Wasiepile
•
§
•
Surface
impoundment
Aerated lank
Pathway
Aft H-Air dep ovl to water
Aq Used-Air dep ovl to sediment
Tor Ill-Air dep to soil
Terr V-Air dep to plant
Aq I-Air diff to water
Aq Ised-Air diff to sediment
Aq Il-Air dep to water
Aq Ilsed-Air dep to sediment
Aq lUsed-Ovl to sediment
Aq IDsed-Ovl to sedimenr
Tejjf l-Dir soil (on)
Ten- Il-Ovl to soil
Terr Ill-Air dep to soil
Terr IV-Air diff to plant
Terr V-Air dep ib plant
Aq I-Air diff to water
Aq Ised-Air diff to sediment
Aq 11-Air dep to water
Aq Ilsed-Air dep to i sediment
Aq ID-Ovl to water
Aq lOsed-Ovl to sediment
Terr Il-Ovl to soil
Terr Ill-Air dep to soil
Terr I V-Air diff to plant
Terr V-Air dep to plant
Aq I-Air diff to water
Aq Ised-Air diff to sediment
Aq JEMDvi to water
Aq IDsed-Ovl to sediment
Terr IV-Air diff to plant
Aq I-Air diff to water
Aq Ised-Air diff to sediment
Terr IV-Air diff to plant
Exposure section
5.3.2.2
5.3.22
5.3.4
5.3.4J
5.3.2.2
5.3.2.2
5.3.2.2
5.3.2.2
5.3.2.2
5.3.2.2
5.3.4
5.3.4
5.3.4
5.: 4.3
5.3.4.3
5.3.2.2
5.322
5.3.22
5.3.2.2
5.3.2.2
5.3.2 2
5.3.4
5.3.4
5.3.4.3
5.3.4.3
5.3.2 2
5.32.2
5.322
5.3.22
5.3.4.3
5.3.2.2
5.32.2 .
5.3.4.3
Fate & Transport
6.52.3
6.52.3
6.32.3
6.6.12.1
6.52.1
6.52.1
6.52J
6.5.2.3
6.52.2
6.522
6.3.2.1
6.322
6.32.3
6.6.122
6.6.12.1
6.5.2.1
6.5.2.1
6.5.2.3
6.52.3
6.522
6.522
6.322
6J2J
6.6.12.2
6.6.12.1
6.52.1
6.5.2.1
6.522
6.52.2
6.6.1.2.2
6.5.2.1
6.5.2.1
6.6.1.2.2
section WMU section
7.2.4.1 and 7.2.5.2
72.4.1 and 7.2.5.2
72.4.1 and 7.2.5.2 '
72.4. 1 and 7.2.5.2 i '
7.3.42, 7J.5.I. and 7.3.6
7.3.42. 7.3.5.1, and 7.3.6
7.3:4.2. 7 J.5.2. and 7.3.6
7.3.42. 7.3.5.2. and 7.3.6
7.3.5.3. and 7.3.6
7.3.5.3. and 7.3.6
7.3.5.3. and 7.3.6
7 J.5.3. and 7.3.6
73.42. 7.3.52. and 7.3.6
7.3.42. 7.3:5.1, and 7.3.6
7.3.42. 7.3.52. and 7.3.6
7.4.4.2 and 7.4.5.2
7.4.42 and 7.4.5.2
7.4.42 and 7.4.5.1
7.4.42 and 7.4.5.1
7.4.5.3
7.4.5.3
7.4.5.3
7.4.42 and 7.4.5.1
7.4.42 and 7.4.52
7.4.42 and 7.4.5.1
7.5.42. 7.5.5.1. and 7.5.6
7 .5.42. 7.5 J.I. and 7.5.6
7 J.5.2 and 7.5.6
7.5.52 and 7.5.6
7.5.42. 7.5.5.1. and 7.5.6
7.6.4.2 and 7.6.5.1
7.6.4.2 and 7.6.5.1
7.6.4.2 and 7.6.5.1
o
S
o
o
09
n
n
r
n
F
i
•2.
TO
o
Q.
I
O
03
f
.1.1
-------
2.0 GUIDE TO BACKCALCULATIONS
22 Roadmap to Backcalculations
Pathways 2a, 2b
Adult Resident Worker
Inhalation ]
Air Concentration at Receptor
Stt.522
2a
2b
Vdatiles
Particulates |
Dispersion.
S«e. 7.3.42 (LAU)
S*c. 7.4.42 (WP)
See. 7.5.42(81)
S«e. 7.8.42 (AT)
Dispersion
Sw. 72.52 (AMF)
Sw. 7.3.42 (LAU)
See. 7.4.42 (WP)
Volatile
Emissions
Sw. 7.3.5.1 (LAU)
Sw. 7.4.52 (WP)
Sw. 7.5.5.1 (SD
S«. 7.8.5.1 (At)
Paniculate
Emissions
SK. 72.4.1 (AMF)
Sw. 7.352 (LAU)
SM. 7.4.5.1 (WP)
Waste Concentration
Partitioning
S«c. 7.3.6 (LAU)
Sw. 7.5.6 (SI)
Figure 2-6. Roadmap to equation sections for air pathways.
-------
2.0 GUIDE TO BACKCALCULATIONS
2.2 Roadmap to Backcalculations
Pathways 3,4,5,6, Terr I, Terr II, Terr'lll
Adult Resident
Child Resident
Terrestrial Ecosystem
3,4
Adult Resident
Child Resident
Worker
Terrestrial Ecosystem
Ingestion
DermaV
Direct Contact
i
Sofl Concentration at Receptor
Sac. 823.1 (Injirten. HUMH)
ac 3 (dm* hunun)
Onsite Exposure |
Son
Son
T*rrl
[Oflsite Bgposurel
4,6,TMria
Sofl
Erosion
B*C. 7.3.5.3. (UU)
Soft
Soft
TNT II
Waste Concentration
Partitioning
SK. 7.M (LAU)
S4C.7.16(3J)
Deposition
from Air
Dispersion
SWL 7.3.4.2 (IAU)
Sw. 7.4.42 CMP)
Partfculate
Emissions
Sw. 72.4.1 (AMF)
Figure 2-7. Roadmap to equation sections for soil pathways.
August 199S
2-17
-------
2.0 GUIDE TO BACKCALCULATIONS
2J Roadmap to Backcalculations
Pathways 1,14
Adult Resident
Adult Resident
Child Resident
Ingestion
-.
Dermal
•
Groundwater Concentration at Receptor
Sec. 5.2.4.1 (ingestion)
Sec. 5.2.42 (dermal)
Leaching
Not covered in this document
Wgste Concentration
Not covered in this document
Figure 2-8. Roadmap to equation sections for groundwater pathways.
4 .._..^ iaoc
2-18
-------
1
2.0 GUIDE TO BACKCALCULATIONS
Roadmap to Backcalculations
Pathways 17,19,20,37,38,42, Aq I, Aq II, Aq III
M&
UU
Adult Resident
Aquatic Ecosystem
^
17,18,20
r •
Adult Resident
Child Resident
Aquatic Ecosystem
Ingestion
^37.38.42
Dermal/
Direct Contact
Aql.ll.IU
Surface Water Concentration at Receptor
S«c. 12,4.1
17.37.Aql
Dispersion
S«e. 7.3.42 (LAU)
SK. 7.4.4^ (WP)
S«. 7.5.44 (SI)
SK. 7.0.42 (AT>
VolatOa
Emissions
SML7.1&KLMJ)
Sw. 7.4.12 (WP)
S«. 7 5.11 (SJ)
Overland Row/
Soil Erosion
SM.ft.542 (tram WMU>
Sac O.S2.3 (tram
SM. 7.5.12 (ipfl Iran SI)
Paniculate
Emissions
SM. 7.14.1 (AMF)
SM. 7.112 (VAU)
S4C. 7.4.11 (WP)
Waste Concentration
Partitioning
SK. 7.16 (LAU)
S4C.7.1«(SI)
19,42, Aq III
Figure 2-9. Roadmap to equation sections for surface water pathways.
-------
2.0 GUIDE TO BACKCALCULATIONS
2.2 Roadmap to Backcalculations
Pathways, 8,8a, 9
1
>
Fn
8,9
Root Uptake
Sw. 6.6.1 2.3 (on)
Ste. 6.6.1 2.4(o«)
i
<
Home gardener
Subsistence fanner
1
1 Ingestjon |
jit/Vegetable
S«c.
>
.Concentraliof
52,5 ^.
f *
Deposition
onto Plant
(aDuvtgrouno VURB
and v«g«tttalM only)
S4C. 6.6.1 2.1
1 Soil Concentration I
Son
r '•
Soil Erosion
S4C. 7.35.3 (IAU)
S«c. 7.4.5.3 (WP)
• off
Deposition
onto Soil
See. 6.6.1 2.1
X
)
r
Dispersion
^ Sec. 7.3.42 (CAU)
S4C. 7.4,42 (WP)
i
i*
Direct
Air Uptake
(abov«Qround feuta
1
Air Concentration
.ofVolatJles
SM. 6.6.122
1
f
Dispersion
S4e 7.3.42 (IAU)
Sw; 7.4.42 (WP)
S«.7.5.42(Sn
SM. 7.6.42 Wft
1 '.*•
*^M**i+***dtm
f,\ •:•:.* : ^5^
M^vpt
ParticuJate
Emissions
S4C. 72.4.1 (AMf)
S4C 7.3.32 (LAU)
S4C. 7.4.5.1 (WP)
i
Waste Cooeflntration
Parttt
toning 4
•voiatJM .
Emissions.
SM. 7.4.52 (WP)
S4C 7.5.5.1 (SI)
S4C. 7.6.5.1 (AT)
•
Figure 2-10. Roadmap to equation sections for plant pathways.
-------
2,0 GUIDE TO BACKCALCULATIONS
22 Roadmap to Backcalculations
Pathways 10, lOa, 11
>
-
11 on
r
Soil Erosion
SM. 7.3.5.3 (LAU)
S«c 7.45.3 (WP)
11 Oft
Subsistence Farmer
i
Ingestion I
V
Concentration in Animal Tissue
Ste.S.2.6
*
P j^BioconcentrationJ .
i
In Forage " '10 ~-
V 1 10, 11 . _
SM.e«£2J(on) < 1 Root Uptake onto
S*e.8.&22.4(ol) " " ^j*j
•I".
Deposition
onto Soil
SM.4.6Z2.1
Dispersion
^ S«* 7.14i (LMJ) ^
SW. 7.4.44 (WP)
L_, >
r
asition Direct
Plant Air Uptake
i
^
Air Concentration
ofVblatiles
>
Dispe
SM.72.4
S«.7.3.«
S«x7.4.<
Sw.75>
r
irswn
U(LAU)
1.2 (WP)
utso
«(AT)
^
Paniculate
Emissions
SM. 7i4.1 (AMT)
SM.7!45.1(WP)
*
•^ ** SM. 7.3.6 (LAU) "*
^ • S»7A6(SI)
* ft ^-t t- _^ ^M! MMdb^rff^MMdt^
Volatile
Emissions
SM.7.2J.1 (LMJ)
SM.7JJ2(WP)
SM. 7.4A1 (SQ
SM; 755.1 (AT)
\
Figure 2*11. Roadmap to equation sections for animal pathways
involving air and soiL
2-21
-------
2.0 GUIDE TO BACKCALCULATTONS
2.2 Roadmap to Backcalculations
Pathways 33,35,36
Subsistence Farmer
Ingestion
Concentration in Animal Tissue
$*.&£*
Bioconcentration
-_J
Surface Water Concentration
SM.M22.7
33
f . • '
Diffusion .
from Air
SM.6.6.2£,S . .
>
r
Dispersion
SM. 7.3.4.2 (LAU)
SM.7.4A2(WP)
8*0.7.5.4.2(31)
SM. 7.6.44 (A1)
*
Volatile
Emissions
Sw. 7^5,1 (LAU)
Sm7.4A2(WP)
SM.7.s.s.i
-------
2.0 GUIDE TO BACKCALCULATIONS
' 2.2 Roadmap to Backcaiculations
Pathways 21,23,24
Fish Consumer
Subsistence Farmer
I Ingestion |
^
Fish Concentration at Receptor
ScaU.7
Btoconcentration
Sedmenta
Concentration
SM. 1112.1
SM.M.1Z3
Surface Water Concentration
21
OWuaion
from Air
Sac.SA.12.1
Overland Flow/
SoilEroeion
S«.C8JLi2(tromW(MU)
S«. B.6JLZ3 (tan
Otspersion
S«.7JL42(LAU)
Set7.4*2 (W(P>
Figure 2-13. Roadmap to equation sections for fish pathways.
2,23
-------
2.0 GUIDE TO BACKCALCULATIONS 2J. Example Backcalculations
2.3 EXAMPLE BACKCALCULATIONS
•••- %• — »
This section provides two example backcalculations worked through from risk to waste
concentration. These examples demonstrate how the equations in the different sections of this
document can be pieced together to calculate the waste concentration. Each of the two
example backcalculations shows the entire backcalculation for a single chemical through a
single exposure pathway for a single waste management unit (WMU).
In the example, the steps to calculate risk-based waste concentrations are presented- as
in the three major sections of this document:
• Exposure (Section 5)—Backcalculates an acceptable concentration in various media
from a target cancer risk of 10"6 or .a hazard quotient of 1 (for humans) or from a
hazard quotient of 1 based on an ecotoxicological benchmark (for ecological
receptors). The exposure media include air, soil, water, vegetables, beef, milk, and
fish. .
• Fate and Transport (Section 6)—Backcalculates from die media concentration to a
waste management unit release or emission rate.
• Waste Management Unit (Section 7)—Backcalculates from the* release or emission
rate to the waste concentration.
The equation numbers from those sections have been retained for the example. The
equations are presented in the table format used throughout the document, i.e., the equation is
shown in the top half of the table, and the input parameters and values are listed in the
bottom half of the table. However, the tables for the example backcalculations contain
several minor changes:
• The equation is presented in symbol format and repeated with values shown instead
of symbols. The calculated result is also shown. •
• Values calculated by other equations are shown in the input list, along with the
reference to the equation that calculates it
• Chemical-specific values fdPthe chemical used in the example calculation are listed
in the input list (throughout the rest of the document these parameters are merely
. noted as chemical-specific because values for all chemicals modeled cannot feasibly
be shown in the tables).
«• > - • ,.
• Parameters specific to the WMU and used in the example backcalculation are also
listed in the input list (throughout the rest of the document these parameters are
merely noted as WMU-specific). ;
-------
2.0 GUIDE TO BACKCALCULATIONS 2J. Example Backcalculations
• Throughout the document, both central tendency and high-end values are shown
where available. However, the value actually used in the example backcalculation is
presented in bold in this section.
2.3.1 Human Example Backcalculation .
* •'
The example backcalculation for a human receptor calculates a risk-based waste
concentration for hexachlorobenzene based on paniculate emissions from a wastepile that
deposit onto soil and forage in an agricutural field where beef cattle graze; the receptor is a
subsistence farmer exposed via ingestion of contaminated beef. It is important to note that
this concentration is specific to a receptor/exposure pathway/waste management unit/chemical
combination. In summary:
Receptor Subsistence farmer .
Pathway: 10: Digestion (beef) -+ Animal -» Plant -» Soil -> Deposition -»
Air-»WMU
WMU: Wastepile
Chemical: Hexachlorobenzene
Throughout the analysis, four parameters were set at high-end values and all other parameters
set at central tendency or default values. Of these four parameters, two were exposure
parameters and two were either fate and transport or WMU parameters. The following
parameters were set at high-end values for £:; example backcalculation:
«
Exposure: Exposure duration. Fraction of beef contaminated
Fate and Transpon/WMU: Wastepile characteristics. Meteorological location
13.1.1 Exposure . .
Equations 5-60 and 5-62 comprise the exposure portion of this example calculation.
Equation 5-60 backcalculates from the target risk level (10"*) to an acceptable daily intake of
hexachlorobenzene. The daily intake is then used in Equation 5-62 to backcakulate an
acceptable beef concentration for the subsistence fanner.
2.3.1.2 Fate and Transport
**"»
Equation 6-72 is the defining equation for the fate and tranpon in this example
backcalculation. The other equations (6-74, 6-75, and 6-77 through 6-81) are used to
calculate inputs for Equation 6-72. This equation backcalculates from die acceptable beef
concentration described above (Section 5) to a deposition rate of hexachlorobenzene onto the
Held, the air dispersion component of the fate and transport model is specific to the
wastepile and included below in the WMU section.
-------
2.0 GLIDE TO BACKCALCULATIONS
2J. Example Backcalculations
2J.I.3 Waste Management Unit
Equation 7-41 backcalculaies from the deposition rate of hexachlorobenzene described
above (Section 6) to a risk-based waste concentration. Equation 7-41 requires a deposition
rate for particles in addition to the deposition rate for contaminant backcateulated with
Equation 6-72. The deposition rate of particles from the wastepile was modeled using the
Fugitive Dust Model (FDM). Equations 7-42 to 7-43 show the calculation of the four types
of paniculate emissions (wind erosion, vehicle resuspension, blown from, trucks, and
unloading) required by FDM as inputs. Based on those emission rates, FDM generates the
deposition rate for particles used in Equation 7-41.
-------
2.0 GUIDE TO BACKCALCULATIONS
2J. Example BackcakuJations
(Humans-HexachJorobenzene)
Intake via Animal Products: Carcinogens—Subsistence Fanner
/ = TR*AT*36S dlyr* 103 pg/mg
ED^EF'CSForat
UT6 • 70 • 365 • 103
(5-60)
Parameter
I
TR
AT
ED
EF
CSForal
40. 350- 1.6
Central
Default teodeacj High-end
Definitioa ValM valut valut
Intake of contaminant (ug/kg/d) Calculated
Target individual risk level (unitless) 10*
Averaging time (yr) 70
Exposure duration (yr) 20 40
Exposure frequency (d/yr) 350
Oral cancer slope factor (mg/kg/d)'1 IA
Refer to
52.9.1
52.9.1
5.2.9.1
52.9.1
52.9.63
Source: RAGS Part B (US. EPA. 199 la).
2-27
-------
2.0 GUIDE TO BACKCALCULATIONS
2J. Example BackcalcuJations
(Humans-Hexachlorobenzene)
Animal Product Concentration— Subsistence Farmer
F*CR
• 70
0.9 • 57
1.56«-3
(5-62)
Central
Parameter
Definitkw
Derail*
vain*
value
Higb^ad
value Refer to
Concentration in animal product (jig/g)
Intake of contaminant (ug/kg/d)
Calcuiafrd
l.le-3
(From Equation 5-60)
BW
F
Body weight (kg)
Fraction contaminated (unities*)
70
0.4
OJ
5.2.9.1
' 5.2.9.2
CR
Consumption rate of animal product (g/d)
57 (beef)
5.2.9.2
Source: RAGS Pan B (U.S. EPA, 199la); Dioxin l:icumcnt (U.S. EPA. 1994a).
-------
2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalculations
(Humans-HexachJorobenzene)
1
Deposition Rate (Crop Uptake from Deposition—Animal Ihgestion)
&•
[i -
(6-72)
•
Parameter Dtflnitioa
D Average annual combined deposition rate
C«..j Concentration in animal product (ug/g)
Ba Biotransfer factor for animal product (d/kg)
Bt; Soil-plant bioconcentration factor for
forage/hay ([ug/f DW]/Iug/g soil])
k, Soil loss constant (yf1)
t Time period of deposition (yr)
Z Soil mixing depth (cm)
BD SoU bulk density (g/eaf)
Rpi Plant interception fraction foyorage/hay
(unitless)
kp * Plant weathering loss coefficient (yfl)
flOo]-,!^! Jl-«'n>*t.lOO.Ojl
""J'71
Central
tendency Hfefe-ead
value va^ue
Calculated
1.56c-3 (From Equation 5-62)
7.9e-3
0.026
71
. (See Equation 6-74)
20 40
2J (untilled) 1 (unfilled)
1J 12
QS
18
Refer to
6.7.62
6.7.62
6.7.3.3
6.7.3.3
6.7.3.1
6.7.4.1
,6.7.4.1
(continued)
2-29
-------
2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalculations
(Hununs-Hexachlorobenzenc)
Parameter DefinitioB
Central
tendency
value
High-end
value
Refer to
Tune of plant exposure to deposition before
consumption (yr)
0.123
Crop yield for forage/hay (kg DW/m2)
024
8.8
6.7.4.1
6.7.4.2
OP,
Quantity of forage eaten by beef cattk
(kgDW/d)
Fraction of forage grown in contaminated
area (unitleu)
6.7.4.2
Quantity of soil eaten by beef cattle (kg/d)
6.7A2
Source: EM (U£. EPA, 1990e; 1993a).
-------
2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalculations
(Hununs-Hexachlorbbcnzene)
Soil Loss Constant
*,=*,/
k, « 0.0048 +0+71-71
(6-74)
Parameter Definition
k. Soil loss constant (yfl)
k,, Soil loss constant due to leaching (yfl)
kb, Soil loss constant due to degradation (yfl)
k,. Soil loss constant due to volatilization (yr'1)
Central
tendency High-end
value value
rak-ulatMi
«
0.0048
(See Equation 6-75)
0
71 (See Equation
6-77)
Refer to
6.7.6.1
Source: EM (U.S. EPA, 1990e: 1993a).
2-31
-------
2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalculations
(Humans-HexachJorobenzene)
Soil Loss Constant Due to Leaching
0«Z»
9
(6-75)
27
Parameter
k,,
q
e
z
BD
jr/
0.3 • 2.5 -Fl M.
Definition
Soil loss constant due to leaching (yf1)
Average annual recharge (cm/yr)
Soil volumetric water content (mL/cm3)
Soil depth from which leaching occurs
(cm)
Soil bulk density (g/cm3)
Soil-water partition coefficient (mL/g)*
-^J
Central
tendency
value
Calcul
27 (Philadelphia)
OJ
2J (unfilled)
IJ5
1,500
. Hick-end
value
qtxf
27 (Liacota) .
(See Equation
6-76)
1 (unfilled)
12
Refer to
6.7.33
6.7.3.1
6.7.6.1
• K, » K^ . f, « 155e+5 - 0.006- IJOO.
Source: EM (US. EPA. 1990e; 1993aV
-------
2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalculations
(Hununs-Hexachlorobenzene)
SoU Volumetric Water Content
0=9,
(6-76)
8 « 0.43
.43 [ JL"
[3,600
i
2 • 5.4*3,
0.3
Parameter
DeflnitkM
Central
tendency
valot
Hlgh^md
value
Refer to
Soil volumetric water content (mL/cm3)
Calculated
Soil saturated volumetric water content (mUcm3)
OJS
6.7.3.1
Average annual recharge rate (cm/yr)
27
(Philadelphia)
27
(LlncoU)
Sanmted hydraulic conductivity (cm/yr)
Soil-specific exponent representing ' *er
retention (unities)
3,600
54
20,000
6.7.3.1
6.7.3.1
Source: SEAM (U.S. EPA. 1988*).
.<«,«4 1OQ<
2-33
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2.0 GUIDE TO BACKCALCULATIONS
2J. Example BackcaJculations
(Humans-Hexachlorobenzene)
Soil Loss Constant Due to Volatilization
(6-77)
170 • 0.42 » 71
Parameter
DefloitkM
Central
teodncy High-«ad
vmtae vahM
Refer to
Soil toss constant due to volatilization (yfl)
Equilibrium coefficient (sfcm-yr)
Gas-phase mate transfer coefficient (cm/s)
Calculated
170 (See Equation 6-78)
0.42 (See Equation 6-79)
Source: EM (U.S. EPA, 1990e: 1993a).
-------
2.0 GUIDE TO BACKCALCULATTONS
2J. Example Backcalculations
(Humans-Hexachlorobenzene)
Volatilization Equilibrium Coefficient
3.1536 •1075/yr«103I/m3»//
(6-78)
3.1536>+10 • 7.5«-4
Parameter
K,
H
z .
*a
R
T
BD
' 2.5 • 1,500 • 8.21e-2
DefloltkM
Equilibrium coefficient (s/cmtyr)
Henry's law constant (atnvm3/mol)
Soil depth from which volatilization occurs
(cm)
Soil water partition coefficient (mL/g)*
Ideal gas constant (atm-L/mol-K)
Temperature (K)
Soil bulk density (g/cm3)
• 298 • 1.5
Central
tendency High-end
value vahM
Calculated
1 ^f A
t «j^^^
2J (untilkd) 1 (untilled)
1^00
821 xlO-2
298
IS 12
Refer to
6.7.6.1
6.7.3 J
6.7.6.1
6.7.7
6.7.22
6.7.3.1
• K4 » K^ - f^ « 2J5e*5 • 0.006 - 1,300.
Source: EM (U.S. EPA, 1990e; 1993*).
imict 1QQ4
2-35
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2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalcuiations
(Hunians-Hexachlorobenzene)
Gas-Phase Mass Transfer CoefTicient
(6-79)
K, - 0.482 * (5.1)0'78 • (2.4)-0'67 • (l.
0.42
Parameter
Deffcitk*
Central
teodeacy Higk-cad
value value
Refer to
Gas-phase mass oansfer coefficient (ctn/s)
Calculated
Windspced (tn/s)
4.6 (Philadelphia)
5.1
(Li«cota)
Sc0
Schmidt number on gas side (witless)
14 (See Equation 6-80)
Effective diameter of contaminated .area (m)
1.600 (See Equation 6-81)
Source: EEM (US. EPA. 199Qe; 1993a).
-------
2.0 GUIDE TO BACKCALCULATIONS
2.3. Example Backcalculations
(Hununs-HexachlorobenzeiK)
Schmidt Number on Gas Side
Sc,
"a "a
(6-80)
Sc,
I.24?-3 • 0.064
2.4
Parameter
Definition
Central
tendency
value
Hlga-ead
vatat
Refer to
Schmidt number on gas side (unities*)
A.
P.
Viscosity of air (g/cm-s)
Density of air (g/cm3)
l.gle-4
6.7.7
6.7.7
Diffusivity in air (cm2/s)
0.064
6.7.6.1
Source: EM (U.S. EPA. 1990e: 1993a).
August 1995
2-37
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2.0 GUIDE TO BACKCALCULATIONS
2 J. Example Backcakulations
(Humans-Hexachlorobenzene)
Effective Diameter
(6-81)
4 •2.000,000
3.14
1,600
Parameter
DefiottkM
Central
tendency
value
vahM
Refer to
Effective diameter of
contaminated area (m)
Calnilard
Area of contaminated area
2,000^00 (field* 300000 (field) 6.7.3 J
Source: IBM (U.S. EPA, 1990e; 1993a).
-------
1
2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalculations
(Humans-Hexachlorobenzene)
Ash Wastepile: Ash Concentration from Deposition Rate [Exit Criteria]
COSH*-
(7-41)
'ash
le-6
0.0045
106 = 1,600
Parameter
Definitioo
Central
tendency High-end
value value
Refer to
Concentration in ash (mg/kg)
Calculated
Backcakulated deposition rate of
contaminant Oig/m2/s)
7e-6 (From Equation 6-72)
Modeled deposition rate of particles over
agricultural field (pg/m2/s)
0.0045
2-39
-------
j. 2J. Example Backcalculations
2.0 GUIDE TO BACKCALCULATIONS ' (Hununs-Hexachlorobenzene)
Ash Wastepile: Emissions from Wind Erosion
(7-42)
Total emissions = E^ • A 1.9e-5 • 5,300 • 0.1 g/s
Parameter
«*. .
*«-
S
P
f
A
Definition
Emissions of PM,0 from wind
erosion (g/m2/s)
Particle size multiplier to adjust
results to PM10 or PM30
Silt content of ash (%)
Number of days per year with
>0.01 in. precipitation
Frequency of wind >5.4 m/s (%) _
Area of wastepite (m2)
Central
tendency
value
High-end
vatae
Refer to
Calculated
0.36 (PM,o)
6.7
117 (Philadelphia)
,, 25.6 (Philadelphia)
120
' • . • .
91 (Lincoln)
91.1 (Lincota)
5300
7.7.33
7.4.3
7.7.1 •
7.7.3.5
7.7.1
Source: AP-42 (U.S. EPA, 1985b).
August 1995
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2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalculations
(Humans-Hexachlorobenzene)
Ash Wastepile: Emissions from Resuspension from Vehicles
p ,^i •
•fivrt~Lj
JL. M.flL
12 48 2,1
4
365
-
86,400
(7-43)
2.3 0.01 in.
precipitation
7.7.3.3
s
v,
w
nw
Roadway silt content (%)
Vehicle speed (km/h)
Vehicle weight (Mg) Loaded
Empty
Average
Number of wheels per vehicle
8
20
10
6
8
6
20
23
10
17.5
10
7.7.3.4
7.7.3.5
7.4.2
7.4.2
117 (Philadelphia) 91 (Lincoln) 7.7.1
N
e
Number of vehicles per day (d~l)
0.2
OJ
2A
7.4.2
7.7.3.6
Dust suppression control efficiency
(unitless)
f
L
Ash fraction on road (unitless)
Length of road (m) <*»
0.1
11
7.7.3.7
73
Source: AP-42 (U.S. EPA, 1985b).
August 1995
2-41
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2.0 GUIDE TO BACKCALCULATIONS
2 J. Example BackcaJculations
(Humans-Hexachlorobenzene)
Ash Wastepile: Emissions from Ash Blown from Trucks
>«Kw^(^V4]{^^}^^
5J ^ 235 J 86,400
' ' . (7-44)
86,400
• L = 4.4e-5 • 73 = 3.2e-3 g/s
.9-0.8
1.5 15 235
Total emission
Parameter
^^MOWQ
^^» iWji
^wra
s
f
p
w
N
e
L
Definition
Emissions of ash (PM10) blown from
tracks (gAn/s)
Particle size multiplier to adjust results
toPM^orPMj,, . .
Silt content of ash (%)
Frequency of wind > 5.4 m/s (%)
Number of days per year with > 0.01 in.
precipitation
Width of truck (m)
Number of ash trucks per day (d* ')
Cover control efficiency (unitless)
Length of road (m)
Central
tendency . High-end
value value
Calculated
036 (PM10)
0 J (PM,,)
6-7
100
1 17 (Philadelphia) 91 (Lincoln)
2 3
02 2A
0.9
11 . ;73
Refer to
7.4.3
7.7.3.5
. 7.7.1
7.4.2
7.4.2
7.7.3.6
7.4.2
Source: AP-42 (U.S. EPA, 1985b).
August 199$
2-42
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2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalculations
(Hununs-Hexachiorobenzene)
1
Ash Wastepile: Emissions from Unloading Trucks
nnnno v ' S u H (M\2 ( Y Y°'33 ..
0.0009 •ATu_f«—•—•—i-« — •— •£,•—
"^ 5 2.2 1.5 (2 j [4.6) 3.
I5e+ls/y
(7-45)
""*" '
Parameter
c
•
*-
S
u
H
M
Y
L
5 2.2 1.5(2 J (4.6
Definitioa
Emissions of ash (PM)0 or PMjg) from
unloading (g/s)
Particle size multiplier to adjust results
to PM10 or PMjo (unities)
Silt content of ash (%)
Mean windspeed
Drop height (m)
Moisture content of ash (%)
Capacity of unloading vehicle (m3)
Loading rate (Mg/yr)
•U.UUUMl
Central
tendency
value
Calcula
OJ6(PMIO)
0.73 (PMjf)
6.7
4.6 (Philadelphia)
2
7.7
4
285
r • » D.ie-i g
High-end
value Refer to
ited
7.7.3J
7.4.3
5.1 (Lincoln) 7.7.1
7.7.6.3
7.4.3
15 7.4.2
13,000 7.4.2
Source: AP-42 (U.S. EPA, 1985b).
2-43
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2.0 GUIDE TO BACKCALCULATIONS 2J. Example Backcalculations
2.3.2 Ecological Example Backcalculation
The example backcalculation for an ecological receptor calculates a risk-based waste
concentration for cadmium based on overland release from a wastepile to a river, the receptor
is a mink exposed via ingcstion of contaminated water and fish. It is important to note that
this concentration is specific to an ecological receptor/exposure pathway/waste management
unit/chemical combination. In summary:
Receptor: Mink
Pathway: Aq ffl: Animal -> Fish/Surface water .-» Overland -> WMU
WMU: Wastepile
Chemical: Cadmium
High-end values for the example backcalculation were used only for the WMU characteristics
and the meteorological location. As discussed in Sections 4 and 5, the use of high-end
exposure inputs for ecological receptors was considered overly conservative. The exposure
scenarios contain several implicit assumptions considered to provide reasonable, conservative
estimates of risk (e.g., 100 percent of mink diet was assumed to be fish).
2.3.2.1 Exposure
Equation 5-115 comprises the exposure portion of this example calculation. Equation
5-115 backcalcuiates from a mink toxicity benchmark to a total surface water concentration.
This equation was modified for cadmium sinrj the bioconcentration factor (BCF) for
cadmium reflects a total, rather than a dissolved, water concentration.
2.3.2.2 Fate and Transport and Waste Management Unit
Equation 6-143 is the defining equation for the fate and tranport in this example
backcalculation. The other equations (6-145 to 6-151) are used to calculate inputs for
Equation 6-143. This equation backcalcuates from the acceptable surface water concentration
described above (Section 5) to a waste concentration at the wastepile. The overland to
surface water model used in this pjathway encompasses both the fate and transport and the
waste management unit release portions of the calculation, as the two cannot be readily
separated in this particular algorithm.
2-44
-------
2.0 GUIDE TO BACKCALCULATIONS
2 J. Example Backcalculations
(Eco-Cadmium)
1
Fish Bioconcentration and Water Ingestion: Total Water Concentration—Mink
benchmark x BW
I x BCF ')
w
(5-115)
C^ - C' „ =
0.82 x 1
187)
0.027 mg/L
Parameter
q,^
benchmark
BW
^
If
BCF*
\
Definition
Total water concentration (mg/L)
Toxicological benchmark for mink
(mg/kg/d)
Body weight (kg)
Intake rate of water for mink (L/d)
Intake rate of fish for mink (kg/d)
Whole-body bioconcentration factor (lAg)
Central
tendency High-end
value value
Calculated
0.82
1.0
0.081
0.16
187
Refer to
Table 5-20
Table 4-4
Table 5-24
Table 5-24
Table 5-24
Table 5-27
August 1995
-------
2.0 GUIDE TO BACKCALCULATIONS
2J. Example Backcalculations
(Eco-Cadtnium)
Waste Concentration: from Total Water Concentration [Exit Criteria]
(6-143)
'waste
0.027 (1.3e+7*0.05+3.3»8.3g+3)«(0.3 +160*1.5) .0.15
0.05•S.aOO-1.5 (0.61 »0.72« 1 • 160» 10'3 +25.• 10'2) O-1^
29,000
Parameter
Cw«e
Cwt
Vfs
fw~
^
V
e
Xd,
BD
A
\
SD
ER
Rf
d.
"•
Definitioo
Concentration in waste (mg/kg)
Total water concentration (mg/L)
Waterbody flow volume (m3/yr)
Fraction of total waterbody contamination in water
column (unitless)
Overall total water concentration dissipation rate
(yr-1)
Flow-independent mixing volume (m3)
Soil volumetric water content (unitless)
Soil-water partition coefficient (cm3/g)
Soil bulk density (g/cm3)
WastepUe area (of)
Unit soU loss (kg/mz/yr)
WastepUe sediment delivery ratio (unitless)
SoU enrichment ratio (unitless)
Average annual runoff (cm/yr)
Depth of water column (m)
Total depth of waterbody (water column and
sediment) (m) ^^
Central -
tendency High-cod
value value
Calculated
0.027 (From Equation 5-1 14)
'Ijt^Q • . | ^^ * ^
JvTO . AwVT *
0.05 (See Equation 6-151)
33 (See Equation 6-145)
6.7e+8 8 Je+3
0.3 (See Equation 6-150)
160
1J U
120 5300
0.61 (See Equation 6-147)
0.72 (See Equation
6-148)
1 (metals)
25 22
0.64 0.15
0.67 0.18
Refer to
6.7.5.1
6.7.5.1
6.7.6.1
6.7.3.1
7.4.2
6.7.3.2
6.7.22
6.7.5.1
6.7.5.1
Source: EM.
Aliens*
-------
2.0 GUIDE TO BAGKCALCULATIONS
2 J. Example Backcalculations
(Eco-Cadmium)
Water Concentration Dissipation Rate
(6-145)
(0.6+1- 17,000) -^21
k * 0. lo
* • ' * * '"oil* ' Tnif
v. . • • j
Central
tendency I
Parameter • Definition . value
k^ Overall total water concentration dissipation Calculate!!
dt. Bed sediment porosity (uhitless) 0.6
BS Bed sediment concentration (mg 1
sediment/L)
Kdfe Bed sediment/sediment pore water partition 160
coefficient (L/kg)
dfc Depth of bed sediments (m) 0.03
dj Total waterbody depth (water column and 0.67
sediments) (m)
Kd,w Suspended sediment/surface water partition 160
coefficient (Ukg)
TSS Total suspended solids (mg/L^ 10
d. Water column depth (m) ' 0.64
Wb Rate of burial (nVyr) 0.1 (See Equation
«.?'V » 13 '
0.03
ligh^iid
value Refer to
'
6.7.52
6.7.52
6.7.6.1.5
6.7.5.1
0.1§ 6.7.5.1
6.7.6.1.4
80 6.7.52
0,15 6.7.5.1
6-147)
Source: IBM.
2-47
-------
2.0 GUIDE TO BACKCALCULATIONS
2 J. Example Backcalculations
(Eco-Cadmium)
Rate of Burial
WA *TSS
W
BS
(0.61-»6g>7»0.13«103)-(1.3>7»10»10-3«103) . 10.in-6
in-3 1
Central
tendency High-end
Parameter
wb
*e
WAL
SD
v*.
TSS
WA,
BS
Definition
Rate of burial (m/yr)
Unit soil loss (kg/m2/yr)
Watershed area (m2)
Watershed sediment delivery ratio (unitless)
Waterbody flow volume (m3/yr)
Total suspended solids (mg/L)
Waterbody surface area (m2)
Bed sediment concentration (kg sediment/L)
value value
Calculated
0.61 (See Equation 6-147)
1 ^AJjQ £.^ *^
• I • JCT3F OC* f
0.13 (See Equation 6-149)
3e+8 lJe+7
10 80
le-WS 4.6«*4
1
Refer to
•
6.7.5.1
6.7.5.1
6.7.52
6.7.5.1
6.7.52
Source: IBM.
AIIOMS*
-------
2.0 GUIDE TO BACKCALCULATIONS
2J. Example BackcalcuJations
(Eco-Cadmium)
1
Universal Soil Loss Equation
Xe*R'K*LS*C*P'9Ql.\*kg/ton'245.1 acre/km2* IQ~*bn2/m2 (6-147)
Xe* 110«0.25• 1 • 0.1 • 1 • 907.18• 245.7• KT6» 0.61
ParaBK
xe
R
K
LS
C
P
•ter Definition
. Unit soil loss (kgAnVyr)
USLE rainfall factor (yr1)
USLE credibility factor (ton/acre)
USLE length-slope factor (unidess)
USLE cover factor (unitless)
USLE erosion control practice factor
(unitless)
Central
tendency High-tad
value value
Calculated
110 300 (Atlanta)
(Portland)
0.25
1 3
0.1 0.3
1
Refer to
6.7.2.2
6.7.32
6.7.3.2
6.7.32
6.7.32
Source: EM.
1995
2-49
-------
2.0 GUIDE TO BACKCALCULATIONS
2-3. Example Backcalculations
(Eco-Cadmium)
Site: Sediment Delivery Ratio
(5,300)-°-125 =0.72
(6-148)
Parameter
Definitkm
Central
tendency Higb-ead
value value Refer to
SD
Sediment delivery ratio (unities*)
Calculated
2.1 2.1
Empirical intercept
coefficient (unitless)
7.7.4.5
Area of wastpik (m2)
120
5300
7.4.2
Source: IBM.
2-50
-------
2.0 GUIDE TO BACKCALCULATIONS
2 J. Example Backcalculations
(Eco-Cadmium)
Watershed: Sediment Delivery Ratio
(6-149)
0.13
Parameter
Definition
Central
tendency High-end
value value •
Refer to
SD
a
Sediment delivery ratio (unidess)
Empirical intercept coefficient (unitless)
Area of watershed (m2)
Calculated
0.6 1 j
1.34e+9 fe+7
6.7.32
6.7.5.1
Source: IBM.
2-51
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2.0 GUIDE TO BACKCALCULATIONS
2 J. Exampk Backcalculations
(Eco-Cadmium)
Soil Volumetric Water Content
e
0.3
(6,150)
Parameter Definition
e
9,
q
K,
b
Soil volumetric water content (mL/cm3)
Soil saturated volumetric water content
(ml/cm3)
Average annual recharge rate (ctn/yr)
Saturated hydraulic conductivity (cm/yr)
Soil-specific exponent representing w T
retention (unitless)
Central
tendency High-end
value value
Calculated
0.43
28 (Portland) 15
3,600
5.4 x
0-55
(Atlanta)
20,000
3.0
Refer to
6.7.3.1
6.7.3.1
6.7.3.1
Source: SEAM.
-------
2.0 GUIDE TO BACKCALCULATIONS
2 J. Example Backcalculations
CEco-Cadmium)
Fraction of Contaminant in Water Column
4
("
/ =
Parameter
^ -\d}
^Kd^TSS' lQ~*kg/mg) •—
(l +160-10- 10-*) •-
;»-™rt«ft)*<«
Definition
dz
.15
Ll8 =«05
Central
tendency High-end
value ' value
\\J-LJl)
Refer to
fWMer Fraction of total waterbody contaminantion Calculated
in water column (unitless) .
*u
TSS
Q.
d,
«b.
Suspended sediment/surface water pa
coefficient (LAg)
Total suspended solids (mg/L)
Depth of water column (m)
^ion 160
10 80
0.64 0.15
Total depth of waterbody (water column and 0.67 0.18
sediment) (m)
Bed sediment porosity (unitless)
0.6
Kdfc, Bed sediment/sediment pore water partition 160
coefficient (LAg) '
BS
\
. . .
Bed sediment concentraoon (seduner
Depth of bed sediments (m)
«/L) 1
0.03
6.7.6.1.4
6.7.52
6.7.5.1
6.7.5.1
6.7.52
6.7.6.1.5
6.7.52
6:7.5.1
Source: IBM.
dualist 199S
2-53
-------
3.0 RECEPTORS
SECTION 3.0
RECEPTORS
3.1 INTRODUCTION
The receptors evaluated in the multiple pathway/receptor analysis were selected to
represent typical and more exposed populations of humans and wildlife.* More exposed
subpopulations (i.e., high-end receptors) were examined to ensure that significant
subpopulations of high-end receptors were protected. For human receptors, potentially more
exposed populations were identified based on the behavior of potential receptors such as
dietary habits and activities (e.g., fish consumption; subsistence farming). For example,
residents living on a closed management unit may be more exposed because of their
proximity to a contaminant source.. In addition, populations involved in certain activities
(e.g., dump truck operators) may be highly exposed to contaminants as a function of their
jobs. For ecological receptors, potentially more exposed populations or communities were
identified for a generic terrestrial and a generic freshwater ecosystem. Within these
ecosystems, receptors were identified based on: (1) the availability of data, (2) the spatial
relationship of the receptor to sinks for envir"-mental contaminants, (3) the behavior of
potential receptors such as dietary habits, and (4) the "representativeness" of the receptor with
respect to various trophic levels. For example, benthic fauna living in intimate contact with
sediments may be highly exposed because sediments often act as contaminant sinks. Sections
3-2 and 3-3 describe the human and ecological receptors selected for the development of exit
criteria, respectively.
3.2 HUMAN RECEPTORS •
3.2.1 Conceptual Approach .
As suggested above, the selection of appropriate receptors was based on either
proximity to a contaminant source or behavior that tends to increase exposure. All receptors
were assumed to live on the contaminSffcd site (Le., on site) or in proximity to the con-
taminant source (i.e., off site) and, therefore, share a spatial characteristic for elevated
exposures. Typical and high-end receptors were identified as a function of certain behaviors
that affect exposure. For example, subsistence farmers are likely to receive higher exposures
to contaminants that accumulate in edible plant tissues than home gardeners due to their
•The term "wildlife" used in this analysis may be defined broadly as living things other than humans or
domesticated animals. In sections on specific receptors, the term wildlife generally refers to"the ecological
receptors being discussed in that section. .
August 1995 3-1
-------
3.0 RECEPTORS
greater intake of contaminated produce. In short, the selection of human receptors reflects the
range of potential receptors that is created by behaviors and activities that increase exposure.
It should be noted that the receptors were evaluated for individual exposure pathways (i.e.,
exposures to multiple1 pathways were not included). An individual-based approach was
chosen over a scenario-based approach because: (1) screening analyses indicated that one
exposure pathway tends to dominate the risk for each constituent, (2) scenarios involving
multiple pathways are plausible but may not be reasonable for more exposed populations (Le.,
a subsistence farmer who is also a subsistence fisher and works at the adjacent contaminated
site), and (3) apportionment among exposure pathways implies that the distribution of
exposure-related behaviors (e.g., home gardening, fishing) is known when, in fact, this
distribution has not been characterized.
The human receptors are described briefly below in terms of the activities that tend to
increase exposure. For each group of receptors, it was assumed that the source of contam-
inants was nearby (Le., receptor at the fenceline) or that the receptors were living on a
contaminated site, as with the closed land application unit Thus, the contaminants may be
transported from the source onto the residential site (Le., off site) or the residents may live on
a contaminated site (Le., on site). Routes of exposure include inhalation of contaminated air,
direct ingestion of contaminated soil and surface water, dermal contact with contaminated soil
and surface water, and ingestion of contaminated plants, beef and milk, and fish. Table 3-1
presents a matrix that illustrates the relationship among the receptors, exposure media (e.g.,
surface water, plants), and route of exposure. The typical receptor is usually an adult
resident High-end receptors include children, workers, subsistence farmers, and subsistence
fishers.
3.2.2 Receptors Exposed to Contaminated Air ,
The receptors assessed for exposure to contaminated air were the adult resident and the
worker. The adult resident was selected as the typical receptor for the inhalation of contam-
inated air. This receptor may be exposed to significant concentrations of contaminated air on
site or off site during day-to-day activities. The worker was selected as a potential high-end
receptor for inhalation based on activities thought to increase exposure such as greater
proximity to the contaminant source.
3.2.3 Receptors Exposed to Contaminated Soil
The receptors assessed for expostfie to contaminated soil were the adult resident, the
child resident, and the worker. Receptors were assumed to be exposed through direct
ingestion of soil and dermal contact The incidental ingestion of soil was considered to be
relevant to both children and adults; both may ingest soil through normal hand-to-mouth
behavior. The adult and child residents were considered as a single receptor with exposure
spanning childhood and adulthood based on daily intake of soil Both child and adult
residents as well as workers were selected as receptors for dermal contact with soiL Young
'children typically spend more time in direct contact with soil than adults (e.gr, playing in the
dirt making mudpies) and have a greater surface area to body weight ratio than adults;
August 1995 . 3-2
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Table 3-1. Human Receptors, Exposure Routes, and Media of Concern
Receptors Air
Adult resident Inhalation (t)
Child resident
Home gardener
Subsistence fanner
Fish consumers
Subsistence fisher $
Worker Inhalation (h)
Exposure media ,.
Soil Groundwater Surface water Plants Beef/milk Fish
Ingestion (t) Dermal (bathing) (t) Ingestion (I)
Dermal (t) Dermal (bathing) (i)
Ingestion (t) Dermal (bathing) (h) Dermal (bathing) (h) • ;
Dermal (h) • '
" Ingestion (t)
Ingestion (h) Ingestion (h)
Ingestion (t)
Ingestion (h)
Dermal (h)
t e Typical receptor.
h = High-end receptor.
g
O
W
5
O
8
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3.0 RECEPTORS
however, for carcinogens, adults may receive a higher lifetime dose due to longer exposure
duration. "
3.2.4 Receptors Exposed to Contaminated Groundwater and Surface Water
The receptors assessed for exposure to contaminated groundwater and surface water
were the adult resident and the child resident As with exposure to contaminated soil,
receptors were assumed to be exposed through direct ingestion of water and dermal contact
The adult resident was selected as the receptor for the ingestion of surface water as a drinking
water source. For dermal contact via bathing, both child and adult residents were chosen as
receptors. Because children have a greater surface area to body weight ratio than adults, they
may be more exposed. However, adults are exposed for a longer duration.
3.2.5 Receptors Exposed to Contaminated Plants ,
The receptors assessed for exposure to contaminated plants were the home gardener and
the subsistence farmer. Adult residents were not included, as they are unlikely to consume a
significant amount of produce from a single contaminated source. Each receptor was assumed
to grow and eat a portion of the household produce on contaminated soiL The high-end
receptor (Le., subsistence fanner) was distinguished from the typical receptor (the home
gardener) by the fraction of total produce ingested that was home grown. However, each
receptor was considered to be a more exposed subpopulation of the adult resident
3.2.6 Receptors Exposed to Contaminated iseef and Mule
The receptor assessed for exposure to contaminated beef and milk was the subsistence
fanner. Unlike receptors for contaminated plants, the number of "home farmers" is relatively
small due to the difficulties and expense of maintaining cattle to supplement other dietary
sources of meat and dairy products. Nevertheless, a more exposed subpopulation does exist
and they raise cattle as their primary source of beef and milk. Therefore, the subsistence
farmer was selected and is considered a high-end receptor.
33.7 Receptors Exposed to Contaminated Fish
The receptors assessed for exposure to contaminated fish were the fish consumer and
the subsistence fisher. Each receptor ^assumed to catch and eat fish from nearby
contaminated surface waters. The fish consumer is a member of the general population who
consumes contaminated fish. The fish consumer and subsistence fisher may be distinguished
by their consumption rate of contaminated fish.
3.2.8 Uncertainties and Issues of Concern
Receptors were identified for typical and high-end exposures, largely on the basis of
behavior and activities that are likely to increase exposure. Although the distributions of
these characteristics (e.g., fraction of home-grown vegetables, daily fish consumption) are
August 1995 3-4
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3.0 RECEPTORS
reasonably well characterized, population distributions for these behaviors and activities have
not been adequately studied. For example, little is known about the percentage of the general
population that consists of subsistence farmers and fishers. Without population distributions
for these receptors, the number of people likely to be exposed to contaminated media cannot
be determined and, therefore, the appropriateness of the receptors cannot be evaluated from
the standpoint of population risk.
3.3 ECOLOGICAL RECEPTORS
3.3.1 Conceptual Approach
The conceptual approach to selecting ecological receptors differs fundamentally from
the approach used to select human receptors. For human receptors, more exposed
subpopulations of one species (humans) were identified at the individual level of biological
organization. In contrast, ecological receptors may occupy different levels of biological
organization (e.g., population, community) and the selection of receptors requires a definition
of. assessment endpoint(s)—an explicit expression of the environmental value that is to be
protected (U.S. EPA, 1992a). The environmental value may be defined at any level of
biological organization and includes specific species deemed important for protection (e.g.,
largemouth bass) and/or species representing specific trophic levels or taxa ecologically
important to an ecosystem. Although assessment endpoints can be defined at any level of
biological organization, they are not equally important at each leveL The individual organism
interacts directly with the environment, is potentially exposed to toxic chemicals,, and
responds to the toxic challenge based on sens^vity, spatial and temporal aspects of the
exposure, and species-specific physiology. The organism-level response may impact the
population if, for example, the dose is sufficiently high to affect a significant number of
individuals in the population, or the exposure impairs the reproductive success of mating pairs
in a population. However, populations exist within communities; they depend on other
populations and other populations likely depend upon them. In turn, the ecosystem is
composed of communities and the physical and chemical environment The interaction
between an organism and a toxic chemical may or may not translate into ecosystem effects,
depending on interactions between the organism and the population, between populations in
the community, all of which is mediated by the physical and chemical characteristics of the
ecosystem. Thus, establishing an operational definition for assessment endpoints requires an
appreciation of the relationship between levels of ecological organization.
In order to better understand aU"$f the issues that frame assessment endpoint selection,
a number of different (and sometimes conflicting) sources were evaluated including:
"Ecosystem Health: L Measuring Ecosystem Health" (Schaeffer et aL, 1988), "What
Constitutes Ecosystem Health?" (Rapport, 1989), "A Critical Appraisal of Population
Approaches in Assessing Fish Community Health" (DeAngelis et aL, 1990), "The Sustainable
Biosphere Initiative: An Ecological Research Agenda" (Lubchenco et aL, 1991), Chemically
Induced Alterations in Sexual and Functional Development: The Wildlife/Human Connection
(Thomas and Colborn, 1992), Framework for Ecological Risk Assessment (UrS. EPA 1992a),
Peer Review Workshop Report on a Framework for Ecological Risk Assessment (U.S. EPA,
August 1995 3-5
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3.0 RECEPTORS
19921), An SAB Report: Evaluation of the Guidance for the Great Lakes Water Quality
Initiative (U.S. EPA, 1992m), "A Critique of Ecosystem Health Concepts and Indexes" (Suter,
1993b), Ecological Risk Assessment (Suter, 1993a), Wildlife Criteria Portions of the Proposed
Water Quality Guidance for the Great Lakes System (U.S. EPA, 1993g), and Draft Ecological
Risk Assessment—issue Papers (U.S. EPA, 1993t). Based on the synthesis of the data,
viewpoints, and recommendations contained in this literature, several basic premises for
assessment endpoint selection were established.
• The smallest level of biological organization that should be assessed is the
reproducing population unless the assessment includes threatened or endangered
species (e.g., U.S. EPA, 1992m; Suter, 1993a)J*
• Sustainability at the ecosystem level is considered an important assessment endpoint,
although consensus is not available on which attributes of the ecosystem constitute
ecosystem sustainability (e.g., Lubchenco et aL, 1991; U.S. EPA, 1992i, m).
• It may be appropriate to consider a suite of assessment endpoints in ecological risk
assessment (e.g., Rapport, 1989; U.S. EPA, 1993t).
• The selection process must explictly recognize the importance of choosing assessment
endpoints that are measurable with available tools and data (e.g., U.S. EPA,
1992a, rn; U.S. EPA, 1993$ Suter, 1993a).t
As stated in Section 1.0, ecosystems ..ay be thought of in general terms as either
aquatic or terrestrial. Although the identification of receptors is more easily accomplished on
an ecosystem-specific basis (e.g., deep water lake, grassland), receptors may be selected to
represent the major trophic elements of generic aquatic and terrestrial food webs. For
example, McVey (1994) presents a classification developed by Schoener (1989) for the major
trophic elements in terrestrial food webs. Seven categories were distinguished based on body
size and dietary preferences:
"Using the reproducing population as an assessment endpoint does not imply that individual variation in
members of the population (e.g., age, sex, physiological state) are ignored. Several authors have developed an
individual-based modeling approach to evaluate populations that accounts for variation in individuals (e.g.,
DeAngdis et al., 1990; Hallam et at, 1993).
tin the jargon of ecological risk assessment^ there are assessment endpoints, defined as explicit expressions
of the environmental value that is to be protected, and there are measurement endpoints, defined as measurable
attributes related to the environmental value chosen as the assessment endpoint (U.S. EPA, 1992a). Although it
is conceptually useful to distinguish between the two types of endpoints, the practical implications in a predictive
regulatory assessment require that both types be evaluated concurrently. In other words, assessment endpoints
are not useful if they do not have measurable attributes, and measurement endpoints are not "useful for assess-
ment endpoints lacking ecological significance, relevance to societal values, or susceptibility to the stressor.
August 1995 3-6
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3.0 RECEPTORS
• Primary producers (i.e., plants)
• Small leaf-or grass-eating herbivores (e.g., numerous arthropods)
• Larger herbivores (e.g., deer, rabbits, voles)
• Small carnivores (e.g., spiders and other predatory insects)
• Medium-sized carnivores (e.g., kestrel)
• Larger carnivores (e.g., fox, wildcats)
• Medium-size omnivores (e.g., raccoons).
The categories provide a useful hierarchy of trophic elements in a generic terrestrial food
web. However, as Schooner (1989) points out, more specific groupings such as granivores
(seed eaters) are not included. In addition, the groupings ignore a critical functional
component of terrestrial ecosystems: organisms that break down organic materials (Le.,
decomposers). More important, the relationship between these categories and the movement
of chemicals in an ecosystem (Le., exposure) is not explicitly addressed.
A similar approach to identifying ecological receptors in the generic ecosystem is to
select receptors to represent each trophic level in a food chain or food web. In this way, the
movement of the chemical is accounted for, starting with the contaminated media, moving
into producers (Le., plants) and lower trophic level consumers (e.g., invertebrates), and
ultimately accumulating in the top predators. This approach is particularly appropriate for
chemicals that bioaccumulate in the food web but may not adequately address exposures to
organisms that live in intimate contact with a contaminated medium.* In addition, it is often
difficult to categorize wildlife into a single trophic leveL Many species of wildlife that are
considered carnivores consume some fraction--f plant matter routinely in the diet The diet
of terrestrial omnivores varies greatly depending upon the season, physiologic state of the
animal (e.g.r whelping), and availability of certain prey items. In short, the trophic level
concept is a somewhat artificial construct developed by "trophic ecologists" to better
understand complex food web interactions. The nature of a food web (and ecosystem) varies
over time and, to some extent,.die spatial distribution of species and the time scale on which
the food web is evaluated. Consequently, the trophic positions of species within; an
ecosystem are in a state of flux. McVey (1994) provides an excellent discussion of the
strengths and limitations of die trophic level approach. •
Clearly, each of the above approaches has advantages. The hierarchical approach based
on body size and diet provides important information on the exposure pathways through
which certain animals are likely to be exposed. The trophic level approach allows the
movement of bioaccumulative contanuD|nts to be tracked through various levels in the food
chain (or web) so that exposures to top predators may be assessed. Each of these approaches
"Other alternatives were also considered. In particular, the use of indicator species to predict adverse effects
at higher levels of biological organization. However, the indicator species approach relies heavily on the. belief
that (1) the most sensitive species has been identified and (2) the relationship between the indicator species and
other species at different levels is transitive (Le., if a = b and b =c, a = c). Although indicator species are
clearly useful and appropriate for some applications (e.g., daphnids for whole-effluent testing), this approach was
considered inadequate to infer protection at the ecosystem level.
August 1995 • 3-7
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3.0 RECEPTORS
has the additional advantage of being amenable to an ecosystem approach; receptors may be
selected to represent categories or trophic levels within a given ecosystem.
For the HWIR analysis, the approach used to select assessment endpoints combines
both the category and trophic level approach described above and adds an additional
component for organisms that are exposed through constant contact with a contaminated
medium. It is essentially an ecosystem approach in that ecological receptors were selected
based on their significance in the ecosystem, their position along a continuum of trophic
levels, and their representativeness of likely exposure pathways. However, while endorsing
an ecosystem approach to ecological risk assessment (U.S. EPA, 1992m), the Science
Advisory Board (SAB) recognized that the human health metaphor cannot simply be
transferred to ecological risk assessment An ecosystem is not an organism with well-defined
boundaries (e.g., skin) and systems (e.g., cardiovascular) and does not have neural and
hormonal mechanisms to maintain homeostasis (Suter, 1993b). Consequently, this approach
does not guarantee the protection of the whole (Le., ecosystem) by protecting some of its
parts (e.g., representative species). It should be emphasized that evaluating endpoints within
the context of an ecosystem does not guarantee that chemical stressors will not cause adverse
impacts at the ecosystem level For example, habitat alterations that occur as a result of a
chemical stressor (e.g., eutrophication) may drastically alter an ecosystem even though direct
effects with respect to the assessment endpoints are not observed.* Unfortunately, metrics
needed to assess indirect impacts on generic ecosystems are currently unavailable and were
not identified. Instead, the response to chemical stressors was evaluated at a lower level of
biological organization (e.g., individual) and protection to higher levels (e.g., community,
ecosystem) was inferred from those response^ These limitations notwithstanding, using a
combined approach to select assessment endpoints within, an ecosystem is a significant step in
protecting that ecosystem.
The framework depicted in Figure 3-1 illustrates the approach used to select ecological
receptors. From virtually any perspective (e.g., bioenergetic to biomass), plants are
considered the foundation of the food web. They are primary producers and, arguably,
represent the only group of receptors that have global significance.. Based on their functional
importance and position in the trophic continuum, terrestrial and aquatic plants were
considered an essential group of receptors.
For each ecosystem, mammals, birds, reptiles and amphibians were considered as
potential receptors. Although reptiles and amphibians may be exposed to chemical stressors,
the toxicological data available with which to evaluate them were insufficient and only a few
species have been sufficiently studied to characterize exposures. Characteristics of potential
species of concern were generally identified in the Wildlife Exposure Factors Handbook (U.S.
EPA, 1993g), the most complete compendium of exposure information available on
Habitat alteration due to the indirect effects of chemical stressors is an extremely important assessment
endpoint Efforts to identify appropriate measurement endpoints for alternative endpoints such as habitat
alteration are currently under consideration; however, appropriate models are not available for a generic analysis.
August 1995 3-8
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3.0 RECEPTORS
Characteristic Assessment Receptors
Blomass and
slonrflcance I—^. Assess foundation —>. Terrestrial (efl^ forage) and
In the food web I of food web structure aquatic plants (e.g., duckweed)
Data availability on I Gather and review data e^effi^alSf168
toxlclty and physical |-r>>(e.g., adverse effects, —>• mammals included
characteristics I • food intake) (e.g., fox, hawk)
Size and dietary I Evaluate dietary preferences d^rertfood'tem?
preference of I—> with respect to potential —>• ETS2S*
consumers I exposures eate'eaSvSnw)
Spatial location I Consider organisms that eSffoS^JSt
In the generic I—-> live in dose contact with —>. fi^*j£SSSf
ecosystem fT a single medium SSwSMT^
Figure 3-L Framework for selection of ecological receptors.
August 1995 3-9
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3.0 RECEPTORS
vertebrates. However, the Handbook represents a small cross-section of potential species of
concern and additional species should be added as characterisic data become available. Thus,
availability of toxicolpgical and physical data was considered as a second cut in selecting
appropriate species. -
The third cut in selecting receptors was size and dietary preference. Including size and
diet was intended to capture the major trophic elements outlined above, but also seeks to
account for the movement of chemicals in the environment For instance, bioaccumulative
chemicals and some metals have been shown to accumulate in earthworms. Therefore,
species that rely on earthworms as a major dietary source of nutrition would be more exposed
through this pathway. In addition, since this analysis has national implications (vs. regional),
a wide distribution across the United States was an important factor in determining the
appropriateness of the species. However, it should be noted that species were not eliminated
from consideration solely because they are found in a single geographic region or are
migratory. Migratory birds were considered as potential receptors even .though they probably
do not live near a contaminant source in both the breeding and wintering habitats because the
birds may be exposed during a sensitive life stage at a "stopover" critical to their migration
route.
The final cut reflects differences in exposure pathways that may result in significant
impacts to certain species of wildlife. Ecological receptors that are in continuous, intimate
contact with the contaminated media may receive high levels of exposure due to constant
exposure to contaminants. Specifically, this applies to communities of organisms that live in
the soil, in the sediment, or are exposed through direct contact with contaminated surface
water.
Based on the framework developed above, a matrix of ecological receptors was
developed according to the route and medium of exposure, as presented in Table 3-2. The
following sections describe the assumptions used to develop the generic ecosystems and the
ecological receptors assumed to live in them.
332 Receptors in Generic Freshwater Ecosystem
Two general types of ecosystems were considered for the generic freshwater ecosystem:
a water-based and a sediment-based ecosystem.4* The water-based ecosystem includes
receptors associated with the limnetic zone (also pelagic zone) typically defined as the region
of open water beyond the littoral zone (see below), and generally characterized by a great
abundance of phytoplankton. The major trophic levels in the limnetic zone are represented by
phytoplankton, zooplankton, small fish (planktivores), larger piscivorous fish, and piscivorous
mammals and birds. Although food chains in freshwater lakes are not generally limited to
*These designations were used primarily to distinguish between types of bioaccumulation models and to
select appropriate parameters for fate and transport modeling (e.g., flow). In the field, organisms may spend
significant portions of their lives in either zone, depending on seasonal variation and life stage.
August 1995 3-10
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Table 3-2. Ecological Receptors, Exposure Routes, and Media of Concern
Expsoure media
Receptors
Surface
water
Sediment
Fish
Aquatic
, Invertebrates
Soil
Soil fauna
Terrestrial
plants
Terrestrial
vertebrates
Mammals
Mink
Otter
Ingestion
Digestion
Ingestion
.Ingestion
Whitetail deer
Eastern cottontail
Ingestion
Ingestion
Ingestion
Digestion
Raccoon
Ingestion
Ingestion Ingestion Ingestion
Deer mouse
Meadow mole
Short-tailed shrew
Red fox
Birds J
Bald eagle
Great blue heron
Herring gull
Osprey
Belled kingfisher
Lesser scaup
Mallard
Spotted sandpiper
Red-tailed hawk
American kestrel
Bobwhite quail
American woodcock
American robin
Other Receptors
'Aquatic plants
I"
Digestion
Digestion
Digestion
Digestion
Digestion
Digestion
Ingestion
Digestion Digestion
Contact
Contact
.
Digestion
Digestion
Digestion :
Digestion
Digestion
Digestion
Digestion Digestion Ingestion
Digestion Digestion
Ingestion Digestion
Digestion Digestion
Digestion
Ingestion
Digestion
Digestion Digestion
Ingestion Ingestion • Digestion
Digestion Digestion
Digestion Digestion
Digestion • Digestion
Ingestion
Benthos
Digestion
direct
Daphnids
Soil organisms
Contact
Plants
Digestion
Direct contact
Direct contact
Digestion
s
s
n
o
g
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3.0 RECEPTORS
limnetic food chains* this water-based scenario was included to represent large lakes
throughout the continental United States. The sediment-based food chain is more common
and represents organisms that ultimately depend on sediment-based organisms that live in the
littoral zone, typically defined as the shallow, marginal region of a lake or stream
characterized by rooted vegetation. Smaller freshwater lakes and streams would almost
certainly possess a relatively large littoral zone and small ponds or streams may consist
entirely of a littoral zone. The major trophic levels in the littoral zone are represented by
phytoplankton/detritus, zooplankton, benthic invertebrates, small forage fish, larger
piscivorous fish, and piscivorous mammals and birds.
Fish and aquatic invertebrates may be highly exposed to contaminated surface water
and sediments through constant, direct contact with gill membranes and via food chain
exposure. In addition, fish bioconcentrate contaminants (particularly hydrophobic chemicals)
and are a source of exposure to piscivorous animals at the top of the food web. Although
some aquatic species are more representative of certain areas of the country than others (e.g.,
salmonids for coldwater lakes and streams), the national exit criteria must be protective of a
variety of species, regardless of their geographic location. Recognizing that only a small
percentage of freshwater species have lexicological data (Seegert et aL, 1986), the freshwater
species identified by Stephan et aL (1985) for the development of national water quality
criteria were included as receptors. The taxonomic families include: Salmonids (class
Osteichthyes), another family in the class Osteichthyes, a third family in die phylum
Chordata, a planktonic crustacean (e.g., Daphnid), an insect, a family in a phylum other than
Chordata or Arthropoda (e.g., Molluscs), and a family in any order of insect or any phylum
not represented in the previous seven desigirrdons. Aquatic plants were also considered as
important ecological receptors. Both algae and vascular plants are crucial to the proper
functioning of aquatic ecosystems (e.g., oxygen production).
The mink and the river otter were selected as ecological receptors representing
mammals in the upper trophic, levels of the freshwater ecosystem. These predatory mammals
are found in a variety of freshwater settings and may be exposed through the food chain or
surface water as their primary drinking water source. Although they represent a small range
in body size and are members of the same taxonomic family, they rely heavily on fish as a
source of nutrition and, therefore, will be highly exposed through the food chain. Muskrats
were also considered as potential receptors since they are typical in a variety of freshwater
ecosystems. They are similar in body size to mink (approximately 1.5 kg) and feed primarily
on aquatic vegetation. However, muskrats were excluded as ecological receptors because: (1)
the dietary exposure could not be evaluated given the paucity of data available on
bioconcentration in aquatic plants.
The representative species of birds are found in a variety of freshwater habitats and
include a range of body sizes across four taxonomic families. Species were selected that rely
heavily on fish or aquatic invertebrates as a primary source of nutrition and will be highly
exposed via ingestion of contaminated prey. The eagle, great blue heron, osprey, and
kingfisher are examples of avian species that may depend almost exclusivelyon fish. The
mallard duck and the lesser scaup primarily consume invertebrates and account for lower
August 1995 . 3-12
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3.0 RECEPTORS
trophic level consumers (i.e., ~ trophic level 3). The spotted sandpiper was included because
it feeds primarily oh sediment dwellers and ingests a high percentage of sediment in its diet
The sandpiper diet represents a "high end" exposure to sediment relative to other aquatic fowl
. (Beyer, 1994). Birds "that consume a large fraction of aquatic vegetation (e.g., Canada goose)
or insects were not included in the list of representative avian species for the same reason that
muskrats were omitted (i.e., the plant or insect exposure pathway required data that were
largely unavailable).
The sediment community was considered a key functional component of the freshwater
ecosystem. The sediments perform important functions in a variety of aquatic ecosystems
(e.g., home to organisms that cleanse water) and contain numerous species of plants and
invertebrates that are key components of aquatic food webs. Li addition, sediments are
important breeding grounds in the larval stages of many insect species. The species
considered for the AWQC were used to represent the sediment community based on the.
assumption that benthic species have a lexicological sensitivity to chemicals that, is similar to
that of water-based species (Di Toro et al., 1991).
3,3 J Receptors in Generic Terrestrial Ecosystem
The generic terrestrial ecosystem was assumed to be a partially forested ecosystem,
consisting of both coniferous and deciduous trees, characterized by sufficient vegetation to
support a variety of wildlife. A, partially forested ecosystem was selected because: (1) this
description applies broadly to many areas throughout the contiguous United States, (2) a
variety of wildlife species are associated with martially forested areas, and (3) it was
consistent with the waste management exposure scenarios modeled for this analysis (see
Section 6). The ecological receptors for the generic terrestrial ecosystem were divided into
three types. Plants were considered as essential ecological receptors since they represent the
primary producers upon which life ultimately depends (and constitute a large fraction of the
biomass on the planet). The second group of receptors included wildlife species across
different trophic levels that do not typically live in the soil (e.g., mammals and birds). The
third group of receptors included wildlife species that live in intimate contact with die soil
Organisms that live in the soil were considered key receptors in the terrestrial ecosystem
because they perform a number of important functions for the ecosystem. For example,
earthworms enrich and aerate the soil to the benefit of plants; dung beetles degrade dead and
decaying matter and are critical to nutrient cycling; and spiders are important predators that
keep the populations of other soil insects in check. The process of selecting receptors for this
group is described in detail in Section "3*3.3.2 since there is currently no precedent within the
Agency to consider soil organisms. ,
333.1 Nonsoil Receptors .
Birds and mammals may be exposed to contaminants in the terrestrial ecosystem
through the food chain or through soil ingestion during grazing and cleaning activities (e.g.,
preening). Food chain exposures involve the bioaccumulation of contaminants in vegetation
or prey items and subsequent ingestion by herbivores or predatory animals, respectively, This
August 1995 3-13
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3.0 RECEPTORS
bioaccumulation may proceed through the food chain, possibly increasing the exposure at
each trophic level (Le., biomagnification). Alternatively, the soil organisms may accumulate
high concentrations of contaminants, resulting in significant exposures for consumers that
ingest a large amount of soil fauna in. the diet (e.g., woodcock, shrew). In addition, plants
may accumulate contaminants from the soil through the root system, through adhesion on the
aboveground surface of the plants, or through air-to-leaf transfer.
Vascular, terrestrial plants were selected as ecological receptors for the generic
terrestrial ecosystem. Due to their importance to the ecosystem and the general lack of data
on plant toxicity, any species of plant was used to represent terrestrial vegetation as long as
appropriate concentration-response data were available. Ideally, plant toxicity,data should
include studies on different genera of plants (e.g., monocots, dicots) for adverse effects to
plant populations. However, toxicity data were seldom available for more than a few plant
species. As a result, representative species were defined very broadly for the terrestrial
plants.
Nonsoil receptors were selected according to die framework illustrated in Figure 3-1.
The lowest trophic level is represented by species that feed primarily on vegetation (Le.,
herbivores or ruminants) and included three mammalian species—the meadow vole, the
cottontail rabbit, and the whitetail deer—and one avian species, the bobwhite quaiL These
species are typical, of most terrestrial ecosystems. A second trophic level is represented by
species that feed primarily on insects and soil fauna and included one species of mammal, the
short-tailed shrew, and one avian species, the American woodcock. These species represent
different dietary habits and, therefore, account for somewhat different exposure pathways
relative to the movement of different chemicals in soil biota. Three wildlife species were
selected to represent opportunistic feeders that would be considered to be in the middle
trophic levels. The deer mouse and the raccoon were selected to represent mammals, and the
American robin was selected to represent birds. Top predators were represented by the red
fox (mammals), and die red-tailed hawk and American kestrel (Le,, sparrow hawk). The red
fox and red-tailed hawk are found throughout most of the United States and consume large
insects (e.g., grasshoppers) and a range of vertebrates from mice to large prey such as
pheasants and snakes. Additional receptors will be added as additional data needed to
characterized food intake, dietary habits, etc., become available.
3333, Soil Community
Soil communities are made up (^numerous groups of species performing one or more
functions for the community, and the development of a "representative" species set proved to
be a formidable task. In order to represent the soil community, a set of species was required
that would capture the breadth and variety of taxonomic and functional groups. A primary
literature review was conducted and several key sources were identified, including Soil
Biology Guide (DindaL 1990), Grassland Invertebrates (Curry, 1994)* Ecology of Soil
Organisms (Brown, 1978), and Ecotoxicology of Soil Organisms (Donker et aL, 1994).
August 1995 3-14
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1
3.0 RECEPTORS
Based on the synthesis of information on soil community structure and function, five
metrics were identified that provided a practical guide to select appropriate species of soil
invertebrates: (1) organism size, (2) spatial location in the soil, (3) abundance relative to
other species, (4) role and importance in the soil trophic structure, and (5) contribution to
energy metabolism. Where possible, organisms were rated under each of the five metrics
according to taxonomic order as described below:
• Size—Soil fauna are often classified into microfauna, comprised of Protozoa,
Nematoda, and other organisms under 0.15 mm; mesofauna, comprised of
Enchytraeidae (pot-worms), Acari (mites), and other invertebrates between 0.16 mm
and 10.24 mm; and macrofauna, comprised of larger invertebrates (Brown, 1978;
Curry, 1994). This convenient, albeit somewhat arbitrary, classification was very
useful when considering physical interactions between the fauna and their habitat as
well as interactions between organisms.
• Location—The vertical distribution of soil organisms-was broken into three groups—
those living in the deep mineral layers, those living in the shallow organic layers, and
those living in the soil litter. The vertical grouping of some organisms depends on
seasonal or diurnal movements within the soil For example, a downward shift in the
soil profile during the summer season occurs in earthworms,, which normally occupy
the superficial soil layers. The degree to which an organism was a permanent or
temporary resident of soil community was also examined by its location at different
life stages.
• Abundance—The number of individuals present in a typical habitat is a classic
parameter for assessing the impacts of contaminants on soil communities. Estimates
of the relative abundance of different taxa were obtained from studies in different
habitats including grasslands and coniferous and deciduous woodlands. However,
species abundance does not always correlate well with the importance of that species
to the community or to a function of the soil. For example, nematodes and annelids
both contribute equally to the flux of CO2, yet nematodes outnumber annelids more
than 100 to 1 (Reichle, 1977).
• Trophic Structure—The relationship between functional types of organisms and the
trophic structure of the ecosystem provided one of the most powerful metrics in this
exercise. Classification by trophic structure accounted for size (Le., larger organisms
usually eat smaller ones), abundance (Le., organisms at the top of a trophic structure
usually are less abundant than those at the bottom), and diversity (Le., different
feeding classes often include different taxa). Based on Brown (1978), organisms
were classified into four functional categories described below and shown in Figure
3-2: • .
— Microphytic—Organisms that feed on fungal spores, hyphae, lichens, and
bacteria. Examples include ants, fungus gnats, nematodes, protozoa, and some
mollusks.
August 1995 3-15
-------
3.0 RECEPTORS
Phytophytes
• Carnivores
Microphytes
Saprophytes
Fungi Bacteria Protozoa
Green plants < > Decaying organic matter
Figure 3-2. Trophic structure of the soil community.
— Saprophytic—Organisms that feed on dead or decaying organic matter.
Examples include earthworms, enchytraeids, millipedes, isopods, acari, and
collembola.
— Phytophagous—Organisms that feed on living plant material including plant
stems, leaves, roots, or woody parts. Organisms classified as phytophytes
include: (leaves) mollusks, insect larvae; (roots) nematode parasites,
symphylids, dipteran, and coleopteran larvae; (woody parts) termites,
coleopteran larvae.
— Carnivorous—Organisms that are true predators, including carabid and
staphylinid beetles, mites, spiders, pseudo-scorpions, centipedes, and some
nematodes and mollusks.
• Energy Metabolism—The relative importance of a species to the overall community
can be based on the contribution of energy that species provides (Curry, 1994). The
starting point for computing energy budgets for entire communities is a list of species
or higher order taxonomic grasps with estimates of their population densities,
biomass, and CO^ flux. *
Although the selection of a representative set of soil invertebrates was complicated by
the sheer numbers and diversity of soil fauna (Le., over 30 Orders in 9 Classes and 4 Phyla of
soil organisms), two key concepts emerged to guide the selection process. First, the members
of a group of organisms that use a resource in a similar way, have similar diets, are found in
similar locations, and behave in the same manner should have similar routes of exposure and
toxicologic response to chemical stressors (i.e., guild theory). Second, taxonomic groupings
are a useful indication of species sensitivity to toxic compounds as demonstrated in numerous
August 1995
3-16
-------
3.0 RECEPTORS
studies on fish (e.g., Suter, 1993a). In addition, limited studies on soil invertebrates indicate
that taxonomically related soil invertebrates have toxicologically similar responses to chemical
stressors (Neuhauseret aL, 1986).
The compilation of data on soil organisms shown in Table 3-3 was evaluated with
respect to the concepts of functional redundancy within the guild and toxicological similarity
with taxa, and a representative species set of eight soil organisms was established. The
selection of eight species to represent the soil community is consistent with the
recommendation of Romijn et aL (1993) as well as the AWQG developed by Stephan et aL
(1985) and is more than recommended by Slooff (1992). Although other representative
species sets are conceivable, the species selected to represent the soil community are a
practical cross-section of potential receptors and cover a broad range of'functions, trophic
levels, and taxa found in soils, including sensitive species such as earthworms. The
representative species requirements are as follows:
• One species from the phylum Nematoda. Nematodes are die most abundant
organisms in the soil and provide the third largest amount of biomass. In addition,
they represent the only microf auna evaluated.
• One species of soil mite (Acarina) from one of the following suborders:
Cryptostigmata, Prostigmata, Mesostigmata, or Metastigmata, Soil mites are
important as decomposers, predators, and plant-eaters. Mites, after earthworms,
comprise the largest portion of animal biomass in the soil and provide the largest
amount of CO^ flux of any of the sou organisms evaluated.
• One. insect from the order CoUembola. Springtails were selected because they are
saprophytic and because they are the second most abundant invertebrates in the soiL
Their high abundance also results in moderately high biomass,
• Two annelids from die orders Flesiopora or Opisthopora, preferably one from each of
the families Enchytraeidea and Lumbricidae. The oligochaeta represent some of die
largest soil organisms and, as subterranean members of the soil community, are
important saprophytic feeders. Members of the Order Opisthopora are the largest
contributor to soil fauna biomass.
• Two additional species of artferppods selected from of the following taxonomic
groups: Diptera, Coleoptara, Isopoda, Qulopoda, biplopoda. As a group, members
of the phylum Arthopoda represent the majority, in terms of all five metrics, of the
species in the soil community.. These five taxonomic groups play, critical roles in the
•• soil community (phytophytes), although they are low in relative abundance.
^
• A species of mollusc from the order Stylqmmatophora. Although the majority of
molluscs are marine organisms, they represent surface decomposers in the trophic
structure that are not duplicated by the other organisms in the representative set
August 1995 3-17
-------
1
oo
Table 3-3. Results
Phylum dan Order
NEMATODA
ARTHROPODA
Arachnid*
Acarina
. • - Araaeae
Scorpionida
. '• (Scorpiones)
1 Chekmethi
' (Pseudoscorpiones)
Phalangida (OpiUoiKs)
Solifugage
Palpigndi
Uropygi
Ambiypygi
Ricinulei
Insecta
Cottembola
. > . Diptera • .
Coleoptara
. . Hymenoptcra
of Data Collection on Soil Community Metrics
Mean
abundance* COjfflux* Blomass*
(no. dm-2) (g m-2 yr-1) (atf ra-2)
>100,000 49.43 950JO
>100 108.08 7.81 1J
+ 1.73 115J
* • . ' • .', ..
+ 0.12 .9.3
+ 0.08 5.8
* "•*
. • + . •
+
+
'+ . .
>1.000 11.13 502J
>10 10.40 849.6
<10 1.99 134X
'-..+ ' tt28 16.9
Trophk level
Microphytic
Omnivorous
Carnivorous
Carnivorous
Carnivorous
.Omnivorous
. Saprophytic .
Phytophagous
Phytophagous
Phytophagous
Size
Microfauna
Mesofauna
Mesofauna
Mesofauna
Mesofauna
Mesofauna
Mesofauna
Mesofauna
Mesofauna
Mesofauna
Mesofauna
Mesofauna
Macrofauna
Macrofauna
Macrofauna
Location
Soil
!
'
Surface
Surface
Surface
Surface
Surface
Surface
Surface
Surface
Surface
Surface
Soil
Example
Roundworms
.
Spiders, miles,
ticks
Miles
Spiders
Scorpions
False
scorpions
Harveslmen
Sun spiders
Wip scorpions
Springlail
Horse flies
(larva)
Beetles
Ants, bees,
wasp
(continued)
3.0 RECEPTORS
-------
Phylum Class Order
Lepidoptera
Orthoplera
Isoptera
Dermaptera
Psocptera
Neuroptera
, Protura
Chiiopda • *
Geophilorpha
Scolopendromorpha
Lilhobiomorpha • .
Scutigeromorpha
Diplopoda
Polydcsmida
Lunacomorpha
Prolerandna
Colobognalha
Netnatophon
Juliforima
Onucomoipha
i SymphyU
Symphyla
Table 3-3 (continued)
Mean
abundance* CO2ffliufc BtomaaV
(no. dm-2) (g m-2 yr-1) (Kg n-2) Trophk kvel
0.10 5.7
•»• • Phytophagous
0.20 26;9
+ . Phytophagous .
+ ' Phytophagous
+ Omnivorous
+ Microphytk
+ Carnivorous
+ 0.28. 8.3 Saprophytic
<10 6.38 32.3 Carnivorous
<10 1^0 249.6 Saprophytic
-
>10 1.82 104.1 Phytophagous
Size Location Example
Moths.
Macrofauna butterflies
Crickets! i
Macrofauna grasshoppers
Macrofauna Termites
Mesofauna Earwig
Mesofauna Plant lice
Mesofauna Lacewigs
Mesofauna
Macrofauna Surface Centipedes
Macrofauna Surface Millipedes
*
Mesofauna Soil
(continued)
t*»
b
s
PI
0
PI
i
o
g
-------
••.
Phylum Class Order
Pauropoda
Pauropoda
Crustacea
Isopoda
Anu^iinoda
MOLLUSCA
Gastropoda
& . Stylommatophora
Basommatophora
ANNELIDA
Oligochaeta
Plesiopora
Opislhopora
* Mean abundance is the average number of individuals of a given
b Annual CO2 efflux for a population (Rekhte, 1977).
c Mean annual biomass (Reichle. 1977).
Note: See text for explanation of trophic level, size, and location.
i
Table 3-3 (continued)
Mean
abundance* CO2fflux* Biomass'
(no.dm-2) (g«-2yr-l) (mg ra-2) Trophic level
+ 0.30 10.3 Saprophytic
<10 ' Saprophytic
•f Saprophytic
<10 1.22 222 .5 Phytophagous
>100 8.90 500.0 Saprophytic
>100 .Saprophytic
Saprophytic
<10 46.70 10,640.0 Saprophytic
laxa for a forest, meadow, and arable soil, V = present (Eijsackers
Size Location Example
Mesofauna Soil
i i
Macrofauna Surface ' Woodlice,
snowbugs
Mesofauna Surface Sandhoppers
Snails, slugs
Macrofauna Surface Snails
Worms
Mesofauna Soil Pot worms
Macrofauna Soil Pot worms
Macrofauna Soil Earthworms
and van de Bund, 1980).
Ul
o
s
l*J
O
M
J
-------
3.0 RECEPTORS
3.3.4 Uncertainties and Issues of Concern
33.4.1 Conceptual Approach
The approach used to select ecological receptors is based on the premise that, if key
components of the ecosystem are protected, then protection will be conferred to the
ecosystem. Although this approach is reasonable given the nature of .the analysis (i.e.,
generic) and the availability of data, protection of measurable endpbints may not adequately
protect all ecologically significant assessment endpoints. For example, amphibians and
reptiles have high ecological significance in certain food webs: amphibians feed on insects
and other invertebrates and are intergral to many aquatic food webs; snakes keep rodent and
insect populations in check and serve as prey for many of the representative species. Thus,
the selection of ecological receptors was ultimately driven by what could be assessed rather
than what should be assessed. It is not possible to determine whether the ecological exit
criteria developed from mammals or fish will be protective of amphibians and reptiles.
Efowever, given the biological difference between classes, it is unknown whether the exit
criteria will afford sufficient protection for all species of amphibians and reptiles.
In addition to data dependency, the structure of the analysis does not consider
alternative endpoints such as habitat alteration. Chemical stressors may produce indirect
changes in structural components of the ecosystem that are not measured by a lexicological
benchmark. For instance, runoff of nitrogen-bearing wastes (e.g., fertilizers) might accelerate
the eutrophication in an aquatic ecosystem due to the increase in turbidity associated with
algal blooms. In short, the ecological receptors evaluated in this report may not include the
most appropriate species for assessing risk at the level of the ecosystem.*
3.3.4.2 Selection of Generic Ecosystems
.Another important premise in selecting ecological receptors was the designation of
generic aquatic and terrestrial ecosystems. Although the ecosystem approach is necessary in
order to select appropriate receptors, it is clear that the generic ecosystems lack the resolution
to identify key components in more "realistic" ecosystems. For example, wetlands and
marine/estuarine ecosystems were not selected because the parameter distributions and
algorithms have not been characterized to model fate and transport in these environments.
This decision does not represent a vates judgment on the importance of marine/estuarine or
wetlands ecosystems. Rather, it reflects the need to develop ecological exit criteria using fate
and transport models and data that are appropriate to generic freshwater. an4 terrestrial
ecosystems. Wetlands are acknowledged to be a "mosaic of important ecosystems" that
provide support functions for natural and living resources and mediate biogeochemical
The indicator species approach provides valuable insights into ecological risk assessment However, a suite
of indicator species that can be used to measure ecosystem sustainability is currently not available. It is
somewhat of a leap of faith to believe that a suite of ecological receptors can be used to represent an ecosystem
or even a community. •
-------
3.0 RECEPTORS
transformations of global significance (Catallo, 1993). Indigenous species of wildlife that are
integral parts of the wetlands ecosystems may not be adequately represented by the suite of
ecological receptors. Moreover, habitat-based endpoints take on additional significance in a
wetlands ecosystem because global and regional functions such as biogeochemical gas
exhanges (e.g., C, O, N, H, and S) may be compromised by chemical stressors.
3.3.4 J Representing Trophic Elements with Single Species
Major trophic elements were represented by a single species, largely dictated by the
availability of data. In particular, plant species were selected based on the magnitude of the
lexicological data relative to other plant species. This approach was adopted for plants
because such a small percentage of species- were identified with lexicological data and
methods to extrapolate across species were not available. Although plants tend to be resilient,
it is highly likely that the chemical-specific plant species is not protective of all important
plants in the terrestrial ecosystem. Moreover, since the plant species selected was often a
food crop, it is not clear whether the ecological exit criteria for crops are relevant to plants in
the wild. Wild plants may be more resistant to contaminated soils because they experience
less controlled conditions (e.g., drought) than food crops in phytotoxicity studies. Conversely,
the food crops may be more resistant due to past exposures to various pesticides (Le., natural
selection may favor more resistant crops).
Similarly, the selection process for ecological receptors relied on a modified trophic
element approach. As a result, representative species were not selected based on their
sensitivity to certain chemicals; they were seJrrted primarily as a function of their ecological
significance and availability of data on their physical characteristics (Le., body weight, food
intake). For the aquatic ecosystem, this appears to be less of a problem since many of the
species included have been shown to be among the most sensitive species (e.g., daphnids).
On the other hand, although daphnids are regarded as one of the most sensitive species of
aquatic invertebrates,.this may be more an artifact of data availability than actual differences
in sensitivity. For the terrestrial ecosystem, the receptors incorporate a wide variety of
exposure pathways. However, species that could be considered as indicators for a trophic
element such as medium carnivores were not identified. Given the variation in lexicological
sensitivity, it seems likely that the ecological receptors used in this analysis do not include the
most sensitive species for each chemical Thus, the ecological exit criteria may be
underprotective for some sensitive wildlife species. Unfortunately, the ecosystem implications
of adverse effects occurring in some sensitive speces are not evident. Data deficiencies
notwithstanding, use of a single speci&to represent other species with different natural
histories, ecological niches, etc., may not provide the most appropriate ecological receptors
for ecological risk assessment
August 1995 3-22
-------
3.0 RECEPTORS
3.3.4.4 Soil Community Approach
Although soil invertebrates may be classified according to ecological function (e.g.,
trophic level, feeding habits), few studies were identified that supported the assumption that
taxonomically related soil invertebrates have lexicologically similar responses to chemical
stressors (e.g., Neuhauser et al., 1986). In addition, many species of soil invertebrates were
excluded that occur only in specialized micro environments such as dung piles, carrion, and
rotting wood (i.e., niche organisms). As a result, species were selected to represent a range
of trophic levels and functions in the community (rather than selecting the "most sensitive"
species). This community-based approach assumes that if key components in the soil
community are protected, that community structure and function will not be adversely
affected. However, this approach has not been validated in field or mesocosm studies, and
there are more than 100,000 species of invertebrates (excluding protozoa) per square
decimeter in forest, meadow, and arable soils (Eijsackers, 1994).
'**,
August 1995 3-23
-------
4.0 ENDPOINTS
SECTION 4.0
BENCHMARKS
4.1 INTRODUCTION
The response of ecological or human receptors to chemical stressors is assessed in
terms of measurement endpoints. For human health risk assessment, the measurement
endpoints are typically defined for adverse effects to individuals that are the result of chronic
exposure to environmental contaminants. These adverse effects include cancer endpoints and
noncancer effects such as damage to a target organ (e.g., kidney, liver) or functional deficits
to a system (e.g., circulatory, immunological). The measurement endpoints used to assess
risks to ecological receptors from chemical stressors may also be defined in terms of
benchmarks. The benchmarks typically reflect both the level of biological organization (e.g.,
individual, population, community) and the desire to ensure the survival of wildlife and the
ecosystem in which they live. Two types of ecological benchmarks were developed for this
analysis: (1) adverse effects that may impair the ability of populations to maintain themselves
(e.g., reproductive effects), and (2) statistically derived concentrations in various media that
may disrupt community structure. By selecr g endpoints reasonably assumed to impact
species at the population level, protection of higher levels of biological organization may be
inferred. Such endpoints include reproductive and developmental effects, as well as other
effects likely to impair the growth and survival of organisms (e.g., mortality). Thus, the level
of biological organization assessed in human health and ecological risk assessments
determines the appropriate measurement endpoints for each group of receptors. For the
human health exit criteria, benchmarks were selected so that the likelihood of any adverse
effect due to a chemical stressor is very small for individuals (i.e., at the individual level of
biological organization). For ecological exit criteria, benchmarks were selected.so that
important populations and communities are protected from chemical stressors.
For human health risk assessment, the Agency has developed guidance on the selection
of appropriate endpoints to calculate health benchmarks for carcinogenic and noncarcinogenic
chemicals (e.g., U.S. EPA, 1987c; Baffles and Dourson, 1988; 56 FR, 63798-63826). These
benchmarks are derived for health effects in any number of categories, including cancer,
reproductive and developmental toxicity, neurotoxicity, and immunotoxicity. In short, the
Agency has specific groups (e.g., Carcinogen Assessment Group) and established protocols
for the development of human health benchmarks. For ecological risk assessment,
lexicological benchmarks analogous to those established for the protection of human health
are not available. Therefore, for this assessment, methods were developed to establish
toxicological benchmarks for the ecological receptors identified in Section 3." It is important
to note that measurement endpoints for human and ecological receptors are not fundamentally
August 1995 • . 4-1
-------
4.0 ENDPOINTS
different Although the human health risk framework cannot serve as a template for an
ecological risk framework, the principle of addressing risks at a specified level of biological
organization is appropriate for both human health and ecological risk assessment Because
humans are valued at" the organism level, measurement endpoints reflect the desire to protect
individuals from adverse effects. In contrast the smallest ecological unit that is persistent on
a human time scale is the population and, therefore, the suite of ecological receptors are
valued from the level of the reproducing population to the functioning community.
Consequently, measurement endpoints were selected to characterize risks to assessment
endpoints from the subsistence fanner to the functional benthic community.
4.2 HUMAN HEALTH BENCHMARKS
4.2.1 Conceptual Approach
• , _' ,
As indicated in the introduction, adverse effects to humans from chemical stressors
were evaluated at the level of the individual. Consistent with the human health risk
framework, endpoints included both cancer and noncancer effects based on studies of chronic
or subchronic duration. Three portals of entry were considered with respect to the exposure
scenarios described in Section 5: ingestion, inhalation, and dermal contact For carcinogens,
the cancer slope factor (CSF) in (milligrams/kilograrn)"1 was used to calculate an acceptable
daily dose at a specified risk level. Because carcinogenic response is assumed to be a non-
threshold phenomenon (i.e., no dose without some finite risk), the target risk level must be
established in order to calculate an acceptable concentration. The target risk level used for
carcinogens in this analysis represented an excess cancer risk of 1 in 1,000,000. In other
words, 1 excess case of cancer would be expected per 1,000,000 people exposed to a
chemical for a lifetime. For noncarcinogens, a reference dose (RfD) for ingestion and dermal
contact in milligrams/kilograms-day and a reference concentration (RfC) for inhalation .in
milligrams/cubic meter were used as the acceptable daily dose and air concentration,
respectively. The ratio between the acceptable daily dose or air concentration and the
exposure level for noncarcinogens was set equal to 1. This ratio, generally referred to as the
hazard quotient (HQ), assumes that no adverse effects will occur as long as the RfD or RfC is
not exceeded.
The toxicological benchmarks—CSF, RfD, and RfC—were identified in two primary
sources: the Integrated Risk Information System (IRIS) and the Health Effects Assessment
Summary Tables (HEAST) developed and maintained by the Agency (U.S. EPA, 1993c, d).
In addition to IRIS and HEAST, otheSsAgency sources such as the Carcinogen Assessment
Group (CAG) profiles, Health Effects Assessments (HEAs), and Health Assessment
Documents (HADs) were used to fill in data gaps for specific chemicals. The health
benchmarks identified for the chemicals of concern are presented in Table 4-1.
4.2.2 Oral Exposures
. t
Oral exposures to contaminated media or food items were evaluated using oral cancer
slope factors in (milligrams/kilpgrams-day)"1 or reference doses in milligrarns/kilograms-day.
August 1995 . 4-2
-------
Table 4-1. Human Health Benchmarks for
CAS
83329
67641
75058
98862
107028
79061
107131
309002
107051
62533
7440360
7440382
7440393
56553
71432
92875
50328
205992
100447
100516
7449417
39638329
117817
111444
Chemical Name
Acenaphlhene
Acetone
Acetonitrile
Acelophenone
Acrolein .
Acrylamide
Acrylonitrile '
Aldrin ^ .
Allyl chloride
• Aniline
Antimony
Arsenic
Barium
Benz(a)anthracene
Benzene
Benzidine
Benzo(a)pyrene
Benzo(fc)fluoranthene
Benzyl chloride
Benzyl alcohol .
Beryllium
Bis (2-chloroisopropyl) ether
Bis(2-«lhylhexyl)phlhalate
Bis(2-chlorethyl)ether
RfD
(mg/kg/d)
value
6.0e-02
l.Oe-01
6.0e-03
l.Oe-01
2.0e-02
2.0e-04
NA
3.0e-05
NA
NA
4.0e-04
3.0e-04
7.0e-02
NA
NA
3.0e-03
NA
NA
NA
3.0e-01
5.0e-03
4.0e-02
2.0e-02
NA
Ref
1 _.
1
1
1
2
1
1
I
1
1
1
2
1
1
1
Oral CSF
(mg/kg/d)-1
value
NA
NA
NA
NA
NA
4.5e+00
5.4e-01
1.7e+01
NA
5.7e-03
. NA
1.5e+00
NA
l.le+00
2.9e-02
2.3e+02
7.3e+00
1.2e+0
1.7e-01
NA
4.3e+00
7.0e-02
1.4e-02
l.le+00
Ref
1
1
1
1
1
TEP
1
1
I
TEP
1
1
2
1
1
HWIR Constituents
WC
(mg/m3)
value Ref
NA
NA
5.0e-02 2
NA
2.0e-05 1
NA
2.0e-03 1
NA
l.Oe-03 1
l.Oe-03 1
NA
NA
5.0e-04 2
NA
NA
NA
NA
. NA
NA
NA
NA
NA
NA
NA
Inhal URF
(pm/m3)*1
value
NA
NA
NA
NA
NA
1.3e-03
6.8e-05
4.9e-03
NA
NA
NA
4.3e-03
NA
NA
8.3e-06
6.7e-02
1.7e-03
NA
NA
NA
2.4«-03
NA
NA
3.3e-04
Inhal CSF
(mg/kg/d)-1
Ref value
NA
NA
NA
NA
NA
1 4.6e+OO
1 2.4e01
1 1.7e+01
NA
NA
NA
1 1.5e+01
NA
NA
1 2.9e-02
1 2.3e+02
H92 6.0e+OO
NA
NA
NA
1 8.4e+00
NA
NA
1 1.2e+00
Ref
C
C
C
C
C
C
C
C
C
(continued)
-------
Table 4-1 (continued)
CAS
75274
75252
71363
88857
85687
7440439
75150
56235
57749
126998
106478
108907
510156
124481
67663
95578
7440473
218019
7440508
106445
95487
108394
98828
Chemical Name
Bromodichloromethane
Bromoform (Tribromomethane)
Butanol
Butyl-4,6-dinitrophenol, 2-sec-
(Dinoseb)
Butylbenzylphthalate
Cadmium*
Carbon disulfide,
'$.
Carbon tetracklovide
Chlordane
Chloro-l,3-butadiene, 2-
(Chloroprene)
Chloroaniline, p-
Chlorobenzene
Chlorobenzilate
Chlorodibromome thane
Chloroform
Chlorophenol, 2-
Chromium VI
Chrysene
Copper
Cresol, p- . '
Cresol, o-
Cresol, m-
Curnene
RfD
(mg/kg/d)
value
2.0e-02
2.0e-02
l.Oe-01
l.Oe-03
2.0e-01
l.Oe-03
l.Oe-01
7.0e-04
6.0e-05
NA
4.0e-03
2.0e-02
2.0e-02
2.0e-02
l.Oe-02
5.0e-03
5.0e-03
NA
3.7e-02
5.0e-03
5.06-02
5.0e-02
4.0e-02
Ref
1
1
1
1
1
1
1
1
1
1
1
1 '
1
1
. 1
1
HEA
1
1
1
1
Oral CSF
(mg/kg/d)-'
value Ref
6.2e-02 1
7.9e-03 1
NA
NA
NA
NA
NA
1.3e-01 1
1.3e+00 1
NA
NA
NA
2.7e-01 2
8.4e-02 1
6.1e-03 1
NA
NA
3.2e-02 TEP
NA
NA
NA
NA
NA
RfC
(mg/m3)
value Ref
NA
NA
NA
NA
NA
NA
l.Oe-02 2
NA
NA
7.0e-03 2
NA
2.0e-02 2
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
9.0e-03 2
Inhal URF
(urn/in3)-1
value Ref
NA
l.le-06 1
NA
NA
NA
1.8e-03 1
NA
1.5e-05 1
3.7e-04 1
NA
NA
NA
NA
NA
-2.3e-05 1
NA
1.2e-02 1
NA
NA
NA
NA
NA
NA
Inhal CSF
(mg/kg/d)-1
value Ref
NA
1
3.9e-p3 C
; NA
NA
NA
6.3e+00 C
NA
5.3e-02 C
1.3e+OO C
NA
NA
NA
NA
NA
8.1e-02 C
NA
4.2e+01 C
NA
NA
NA
NA
NA
NA
(continued)
4.0 ENDPOINTS
-------
Table 4-1 (continued)
CAS"
72548
72559
50293
117840
84742.
2303164
53703
96128
106467
95501
91941
75718
75343
107062
156592
75354
156605
120832
94757
78875
542756
10061026
10061015
Chemical Name
ODD
DDE
DDT
Di-n-octyl phthalate
Di-n-butyl phthalate
DiaUate
Dibenz(a,fc)anthracene
Dibromo-3-chlujopropane, 1,2-
Dichlorobenzene, 1,4-
Dichlorobenzene, 1,2-
Dichlorobenzidine, 3,3'-
Dichlorodifluoromelhane
Dichloroelhane, 1,1-
Dichloroelhane, 1,2-
Dichloroelhylene, ci's-1,2-
Dichtoroethylene,- 1 , 1 ,
Dichloroethylene, trans- 1,2-
Dichlorophenol, 2,4-
Dichlorophenoxyacetic acid, 2,4-
(2,4-D)
Dichloropropane, 1,2-
Dichloropropene, 1,3-
Dichloropropene, frcvu-1,3-
Dichloropropene, m-1,3-
RID
(mg/kg/d)
value
NA
NA
5.0e-04
2.0e-02
l.Oe-01
NA
NA
NA
NA
9.0e-02
NA
2.0e-01
NA
NA
l.Oe-02
9.0e-03
2.0e-02
3.0c-03
l.Oe-02
NA
3.0e-04
3.0e-04
3.0e-04
Oral CSF
(mg/kg/d)'1
Ref value
1
2
1
1
1
2
1
1
1
1
1
1
1
2.4e-01
3.4e-01
3.4e-01
NA
NA
6.U-02
8.1e+00
1.4e+00
2.4e-02
NA
• 4.5e-01
NA
9.1e-02
9.1e-02
NA
6.0e-01
NA
NA
NA
6.8e-02
1.8e-01
1.8e-01
1.8e01
Ref
1
1
1
2
TEF
2
2
1
31
1
1
2
2
2
2
RfC
(mg/m3)
value
NA
NA
NA
NA
NA
NA
NA
2.0e-04
8.0e-01
2.0e-01
NA
2.0e-01
5.0e-01
NA
NA
NA
NA
NA
NA
4.0e-03
2.0e-02
2.0e-02
2.0e-02
. Inhal URF
(pm/m3)'1
Ref value
NA
NA
9.7e-05
NA
NA
NA
NA
1 NA
2 NA
2 NA
NA
2 NA
2 NA
2.6e-05
NA
5.0e-05
NA
NA
NA
1 NA
1 NA
1 3.7e-05
1 3.7e-05
Inhal CSF
(mg/kg/d)*1
Ref value Ref
NA
NA
1 ; 3.4e-01 C
NA
NA
NA
NA
6.9e-07 2
NA
NA
NA
NA
NA
1 9.1e-02 C
NA
1 1.8e-01 C
NA
NA
NA
NA
1.3e-01 2 .
2 1.3e-01 C
2 1.3e-01 C
(continued)
4.0 ENDPOINTS
-------
C
I—
VC
vo
in
CAS
60571 .
84662
56531
60515
131113
57976
119937
105679
119904
99650
51285
121142
606202
123911
122394
1298044
115297
72208
106898
110805
60297
4
141786
62500
97632
Chemical Name
Dieldrin
Dielhyl phthalate
Diethylstilbestrol
Dimethoate
Dimethyl phthalate
Dimethylbenz(a)anthracene, 7,12-
Dimethylbenzidine, 3,3'-
Dimethylphenq^ 2,4-
Dimelhyoxybenzidine, 3,3'-
Dinitrobenzene, 1,3-
Dinitrophenol, 2,4-
Dinilrololuene, 2,4-
Dinilrololuene, 2,6-
Dioxane, 1,4-
Diphenylamine .
Disulfolon
Endosulfan
Endrin
Epichlorohydrin
Ethoxyelhanol, 2-
Elhyl ether
Ethyl acetate
Ethyl methanesulfonate
Ethyl melhacrylate
RfD
(mg/kg/d)
value
5.0e-05
8.0e-01
NA
2.0e-04
l.Oe+01
NA
NA
2.0e-02
NA
l.Oe-04
2.0e-03
2.0e-03
l.Oe-03
NA
2.5e-02
4.0e-05
6.0e-03
3.0e-04
2.0e-03
4.0e-01
2.0e-01
9.0e-01
NA
9.0e-02
IX
Ref
1
1
1
1
1
1
1
1
2
1
1
2
1
2
1
1
1
2
ime i-i i. ton
Oral CSF
(mg/kg/d)-1
value
1.6e+01
NA
4,7e+03
NA
NA
2.5e+01
9.2e+00
NA
1.4e-02
NA
. NA
NA
NA
l.le-02
NA
NA
NA
NA
9.9e-03
NA
NA
NA
2.9e+02
NA
unueuj
RfC
(mg/m3)
Ref value
1 NA
NA
2 NA
NA
NA
CAG NA
2 NA
NA
2 NA
NA
NA
NA
NA
1 NA
NA
NA
NA
NA
1 l.Oe-03
2.0e-01
NA
NA
29 NA
NA
Inhal URF
(urn/in3)-1
• Ref. value
4.6e-03
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1 1.2e-06
1 NA
NA
NA
NA
NA
Inhal CSF 1
(mg/kg/d)-' 1
Ref value Ref 1
1 1.6e+01 C §
NA 1
; NA
NA
NA
NA
NA
,NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1 4.2e-03 C
NA
NA
NA
NA
NA
P^
c*5
O
2
H
C/)
i
(continued)
-------
Table 4-1 (continued)
CAS
100414 •
96457
106934
206440
86737
50000
64186
110009
76448
1024573
87683
118741
319846
319857
58899
77474
67721
70304
193395
78831
78591 .
143500
Chemical Name
Ethylbenzene
Ethylene thiourea
Ethylene Dibromide
Fluoranthene
Fluorene
Formaldehyde.
Formic Acid
Fuian »
Heptachlor *
Heptachlor epoxide
Hexachloro-1 ,3-butadiene
Hexachlorobenzene
Hexachlorocyclohexane, ot-
(a-BHC)
Hexachlorocyclohexane, P-
(P-BHC)
Hexachlorocyclohexane, f-
(Lindane)
Hexichlorocyclopentadiene
Hexachloroethane
Hexachlorophene
Indeno(l,2,3-c/i) pyrene
Isobutyl alcohol
Isophorone
Kepone
RfD
(mg/kg/d)
value
l.Oe-01
S.Oe-05
NA
4.0e-02
4.0e-02
2.0e-01
2.06400
l.Oe-03
5.06-04
1.3e-05
2.0e-04
8.0e-04
NA
NA
3.0e-04
7.0e-03
l.Oe-03
3.0e-04
NA
3.0e-01
2.0e-01
NA
Ref
1
1
1
1
1
2
1
1
1
2
1
1
1
1
1
1
1
Oral CSF
(mg/kg/d)'1
value
NA
6.0e-01
8.5e401
NA
NA
NA
NA
NA
4.5e400
9.16400
. 7.8e-02
1.664OO
6.3e400
1.86400
1.3e400
NA
1.4e-02
NA
4e-01
NA
9:5e-04
4.86401
RfC
(mg/m3)
Ref value
1.06400
2 NA
1 2.0e-04
NA
NA
NA
NA
NA
1 NA
1 NA
1 NA
1 NA
1 NA
1 NA
2 NA
7.0e-05
1 NA
NA
TEF NA
NA
1 NA
27 NA
Inhal URF
(um/mV
' Ref value
1 NA
NA
2 2.2e-04
NA
NA
1.3e-05
NA
NA
1.36-03
2.6e-03
2.2e-05
4.6e-04
1.8e-03
5.3e-04
NA
2 NA
4.06-06
NA
NA
NA
NA
NA
, Inhal CSF
Ref value Ref
NA
M
1 7.7e-01 C
NA
NA 1
1 4.6e-02 C
NA
NA
1 4.6e400 C
1 9.1e400 C
1 7.7e-02 C
1 1.6e400 C
1 6.3e400 C
1 1.9e400 C
NA
NA
1 1.4e-02 C
NA
NA
NA
NA
NA
(continued)
^ . •
4.0 ENDPOINTS
-------
Table 4-1 (continued)
CAS
7439976
126987
67561
72435
108101
74873
80626
78933
74839
298000
56495
74953
75092
7439987
930552
86306
100754
621647
91203
7440020
98953
IL
79469
924163
55185^
Chemical Name
Mercury1*
Methacrylonitrile
Melhanol
Melhoxychlor
Methyl isobutyl ketone
Methyl chloride (Chloromethane)
Methyl methacrylate
Methyl ethyl ketone
Methyl bromide (Bromomelhane)
Methyl paralhion
Methylcholanlhrene, 3-
Melhylene bromide
Methylene chloride
Molybdenum.
A/-Nitrosopyrrolidine
N-Nitrosodiphenylamine
A/-Nitrosopiperidine
/V-Nitrosodi-n-propylamine
Naphthalene
Nickel
Nitrobenzene
Nitrbpropane, 2-
Nitrosodi-n-butylamine
Nilrosodiethylamine
RID
(mg/kg/d)
value
3.0e-04
l.Oe-04
S.Oe-01
5.0e-03
S.Oe-02
NA
8.0e-02
6.0e-01
1.4e-03
2.5e-04
NA
l.Oe-02
6.0e-02
5.0e-03
NA
NA
NA
NA
4.0e-02
2.0e-02
5.0e-04
NA
NA
NA
Ref
RtoC
1
1
1
2
2
1
1
1
2
1
1
H92
- 1
1
Oral CSF
(mg/kg/d)-1
value Ref
NA
NA
NA
NA
NA
NA
NA
NA
NA .
NA
. 2.6e+01 25
NA
7.5e-03 1
NA
2.1e+00 1
4.9e-03 ,1
3.8e+01 30
7.0e+00 1
NA
NA
NA
NA
5.46+00 1
1.5e+02 f
RfC
(mg/m3)
value
3.06-04
7.0e-04
NA
NA
8.0e-02
NA
NA
l.Oe+00
5.0e-03
NA
NA
NA
3.0e+00
NA
NA
NA
NA
NA
NA
NA
2.0e-03
2.0e-02
NA
NA
Inhal URF
(urn/in3)-1
Ref value
2 NA
2 NA
NA
NA
2 NA
1.8e-06
NA
1 NA
1 NA
NA
NA
NA
2 4.7e-07
NA
6.1e-04
NA
NA
NA
NA
NA
2 NA
1 NA
1.6e-03
4.3e-02
Inhal CSF
(mg/kg/d)-1
Ref value Ref
NA
NA
NA
NA
NA
2 6.3e-03 C
NA
NA
NA
NA
NA
NA
1 1.6e-03 C
NA
1 2.16+00 C
NA
NA
NA
NA
NA
NA
9.4e+00 2
1 5.66+00 C
1 1.5e+02 C
• ' •' (continued)
4.0 ENDPOINTS
-------
CAS
62759
10595956
152169
56382
608935
82688
87865
108952
62384
108452
298022
1336363
23950585
129000
110861
94597
7782492
• 57249
100425
1746016
95943
79345
630206
127184
Chemical Name
Nitrosodimethylamine
Nilrosomethylelhylamine
Octamelhylpyrophosphoramide
Paiathion
Pentachlorobenzene
PenUchloronitrobenzene (PCNB)
Pentachlorophenol
Phenol >
Phenyl mercuric acetate
Phenylenediamine, m-
Phorate
Polychlorinated biphenyls
Pronamide
Pyrene
Pyridine
Safrole
Selenium
Strychnine
Stryene
TCDD, 2,3,7,8-
Tetrachlorobenzene, 1.2,4,5-
Tetrachloroelhane, 1,1,2,2-
Tetrachloroethane, 1,1,1,2-
Telrachloroethylene
RfD
(mg/kg/d)
value
NA
NA
2.0e-03
6.0e-03
S.Oe-04
3.0e-03
3.0e-02
6.0e-01
8.0e-05
6.0e-03
2.0e-04
NA
7.5e-02
3.0e-02
l.Oe-03
NA
5.0e-03
3.0e-04
2.0e-01
NA
1 3.0e-04
NA
3.0e-02
l.Oe-02
Re
2
2
1
1
1
1
1
1
2
1
1
1
1
1
1
1
1
1
Table 4-1 (continued)
Oral CSF
(mg/kg/d)-1
r value Ref
5.1e+01 1
2.2e+01 1
NA
NA
NA
2.6e-01 2
1.2e-01 1
NA
NA
NA
, NA
7.7e+00 1
NA
NA
NA
1.8e-01 26
NA
NA
NA
1.6e+05 Diox
NA
2.0e-01 1
2.6e-02 1
NA
RfC
(mg/m3)
value
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
7.0e-03
NA
NA
NA
l.Oe+00
NA
NA
NA
NA
NA
Inhal URF
(urn/in3)-1
Ref value
1.4e-02
NA
NA
NA
NA
NA
NA
NA
NA
NA
. NA
NA
NA
NA
14 NA
NA
NA
NA
1 NA
NA
NA
5.8e-05
7.4e-06
NA
Inhal CSF
(mg/kg/d)-1
Ref value Ref
1 4.9e+01 C
N/y
•• NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1.6e+05 Diox
NA
1 2.0e-01 C
1 2.6e-02 C
NA
(continued)
•b.
b
M
"O
§
a
-------
'-
CAS
58902
3689245
7440280
108883
95807
95534
106490
8001352
76131
120821 -
71556
79005
79016
75694
88062
95954
93765
93721
96(84
99354
126727
7440622
75014
Chemical Name
Tetrachlorophenol, 2,3,4,6-
Tetraelhyldithiopyrophosphate
Thallium (I)
Toluene
Toluenediamine, 2,4-
Toluidine, o-
' Toluidine, p-
Toxaphene jj
Trichloro-l,2,2'-tnfluoroelhane,
1.1,2-
Trichlorobenzene, 1,2,4-
Trichloroethane, 1.1.1-
Trichloroethane, 1,1,2-
Trichloroelhylene
Trichloroflu'oromelhane
Trichlorophenol, 2,4,6-
Trichlorophenol, 2,4,5-
Trichtorophenoxyacetic acid,
2,4,5- (245-T)
Trichlorophenoxypropionic acid,
2,4,5- (Silvex)
Trichloropropane, 1,2,3-
Trinitrobenzene, sym-
Tris (2,3-dibromopropyl)
phosphate
Vanadium
Vinyl chloride
RfD
(mg/kg/d)
value
3.0e-02
S.Oe-04
8.0e-05
2.0e-01
NA
NA
NA
NA
3.0e+01
l.Oe-02
NA
4.0e-03
NA
3.0e-01
NA
l.Oe-01
l.Oe-02
8.0e-03
1 6.0e-03
5.0e-05
NA
7.0e-03
NA
Rel
1
1
1
1
1
1
1
1
1
1
1
1
1
2
Table 4-1 (continued)
Oral CSF
(mg/kg/d)-1
r value Ref
NA
NA
NA
NA
3.2e+00 2
2.4e-01 2
1.9e-OI 2
l.le+00 1
NA
NA
NA
5.7e-02 1
l.le-02 SF
NA
l.le-02 1
NA
NA
NA
NA
NA
9.8e+00 28
NA
1.9e+00 2
RfC
(mg/m3)
value Ref
NA
NA
NA
4.0e-01 1
NA
NA
NA
NA
3.0e+01 2
9.0e-03 2
l.Oe+00 SF
NA
NA
7.0e-01 2
NA
NA
NA
NA
NA
NA
NA
NA
NA
Inhal URF
(urn/m3)-1
value
NA
NA
NA '
NA
NA
NA
NA
3.2e-04
NA
NA
NA
1.6e-05
1.7e-06
NA
3.U-06
NA
NA
NA
NA
NA
NA
NA
NA
Inhal CSF
(mg/kg/d)-1
Ref value . Ref
NA
N^
' NA
NA
NA
NA
NA
1 l.le+00 C
NA
NA
NA
1 5.6e-02 C
SF 6.0e-03 C
NA
1 l.le-02 C
NA
NA
NA
NA
NA
NA
NA
3.0e-01 C
o
W
"Z.
o
o
5
H
C/5
-------
>\
se
VO
•in
Table 4-1 (continued)
CAS
Chemical Name
RfD Oral CSF RfC Inhal URF Inhal CSF
(mg/kg/d) (mg/kg/d)'1 (mg/m3) (um/rn3)'1 (mg/kg/d)'1
value Ref value Ref value Ref value Ref value Ref
1330207 Xylenes (total)
2.0e-fOO
NA
3.0e-01
NA
NA
M
O
S
7440666 Zinc
3.0e-01
NA
NA
NA
NA,
* RfD shown is for food/soil. For water ingestion, the RfD is Se-04.
b RfD shown is for mercuric chloride. For fish pathways, the RfD for methyl mercury (le-04) is used instead.
References
01 = U.S. EPA (1993c)—IRIS
02 = U.S. EPA (1993d)—HEAST
14 = U.S. EPA (1986c) . « '
25 = U.S. EPA (1988c)
26 = U.S. EPA (1988e)
27 = U.S. EPA (1988d)
28 = U.S. EPA (1988g)
29 = U.S. EPA (1988J)
30 = U.S. EPA (19880
31 = U.S. EPA (1985a)
C = Calculated from URF
CAG = U.S. EPA (1988h) & I. S. EPA (19881)
Diox = U.S. EPA (1992c)
HEA = U.S. EPA (1986e)
H92 = U.S. EPA (1992f>-HEAST
RtoC = U.S. EPA (1994b)—Report to Congress
SF = Superfund Program
TEP = U.S. EPA (1993b)
-------
4.0 ENDPOINTS
4.2.3 Inhalation-Exposures
Inhalation exposures were evaluated for volatiles and participates using inhalation
cancer slope factors in (milligrams/kilogranis/-day)"1 or reference concentrations in
milligram/cubic meter. If inhalation data were not available, benchmarks were not
extrapolated from oral benchmarks unless: (1) the benchmark in IRIS was extrapolated across
exposure routes, or (2) it could be demonstrated that no portal of entry or first pass effects
occur.
4.2.4 Dermal Exposures
Dermal exposures were evaluated for contact with contaminated soils, surface water, or
groundwater. If data on dermal contact were not available, benchmarks for dermal exposure
were estimated using the oral cancer slope factors and reference doses, as directed in Dermal
Exposure Assessment: Principles and Applications—Interim Report (U.S. EPA, 1992d).
4.2.5 Uncertainties and Issues of Concern
As stated above, toxicological benchmarks for the protection of human health were
taken from Agency-approved sources such as IRIS and HEAST. Benchmarks contained in
the IRIS database are considered to be."verified" by the Agency and are supported for use in
the risk assessment process. Benchmarks in alternative sources such as HEAST or HEAs are
intended for provisional use pending review by Agency workgroups. The RfD/RfC
workgroups and the Carcinogenic Risk Assessment Verification Endeavor (CRAVE)
workgroups continually review human health benchmarks developed in "unverified" sources
for noncarcinogens and carcinogens, respectively. Pending the results of the workgroup
review process, health benchmarks may be added to the IRIS database or, if the workgroup
findings are inconclusive, the provisional benchmark may be removed from IRIS until further
data become available. In most instances, the value removed from IRIS will remain in
HEAST until a final decision is reached.
To reduce the uncertainty in using unverified benchmarks, the Office of Solid Waste
conducted a review of all unverified cancer and noncancer benchmarks used for chemicals in
the Hazardous Waste Identification Rule (HWIR) analysis. The Review of Unverified Cancer
and Non-cancer Health Benchmark Values for HWIR Constituents (RTI, 1993) established
specific criteria for validity, includin-fsconsistency with current Agency criteria (e.g., study
quality, human relevance) and Good Laboratory Practice (GLP) standards compliance. The
Agency rationale and supporting studies for each of the unverified values were reviewed and
the rationale for used in the selection of appropriate benchmarks was explained. However,
Agency-approved guidance used to derive benchmarks from laboratory and epidemiological
studies is a source of continuing debate within both the Agency and the scientific community
at large. Indeed, a substantial body of literature exists describing the pros and cons of
Agency policies as well as alternative methods that may be used to reduce the uncertainty in
benchmark estimation. While acknowledging these issues, the Agency-approved benchmarks
August 1995 4-12
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4.0 ENDPOINTS
have sufficient support for their inclusion in the HWIR analysis. Therefore, the uncertainty
discussion below-provides only a brief description of sources of uncertainty inherent in the
process of developing human health benchmarks.
4.2.5.1 Noncarcinogens
The current approach in noncancer dose response assessment used by the Agency is a
semiquantitative method to identify exposure levels that are not likely to result in adverse
effects. This method is not a mechanistically based quantitative method for assessing the
likelihood and severity of effects in an exposed population. Ideally, human data would be
used for these assessments. However, they are rarely available to sufficiently identify
thresholds. As a result, animal studies are typically used to identify two toxicity measures.
The no observed adverse effects level (NOAEL) is the highest exposure or dose among all the
individual experimental studies at which no statistically or biologically significant adverse
effects were observed when compared to the control group. The lowest observed adverse
effects level (LOAEL) is the lowest exposure or dose at which there is a statistically or
biologically significant increase in adverse effects. A number of default options are used to
support the dose-response method in the light of competing plausible assumptions and
uncertainty. These default options are intended to lead to dose-response measures that,
although plausible, are believed to be conservative. The following major default options are
used in noncancer dose-response assessment:
• Laboratory animals are a surrogate for humans in assessing toxicity; positive
occurrence of adverse effects in laboratory animals is taken as evidence of a potential
for that chemical to cause adverse etiects in humans, although the types of effects
might not be the same in humans as in animals.
• Humans are as sensitive as the most sensitive animal species, strain, or sex evaluated
in a bioassay with appropriate study-design characteristics.
• Chronic (e.g., 2 years in rats) and subchronic (e.g., 90 days in rats) experimental
bioassay studies are predictive of effects over a lifetime (70 years) of exposure in
humans.
• The NOAEL approximates the threshold of toxicity. The LOAEL identifies the
critical target organ and most sensitive toxic endpoint even though other target organs
may be affected and adverse -sf/ects may appear at higher doses. Doses below the
identified threshold (NOAEL) would also protect against other adverse effects that
may appear at higher doses.
• Dosimetric conversions to human equivalent doses are based on important biological
parameters (e.g., inhalation rate, tidal volume) and on the physical and chemical
properties of the pollutant (e.g., gas, aerosol, reactive). When extrapolating metabolic
data from laboratory animals to humans, one may use the relationship of surface area
in the test species to that in humans in modifying the laboratory animal data.
August 1995 4-13
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4.0 ENDPOINTS
A given unit of intake of a chemical has the same effect, regardless of the time of its
intake; che4nical intake is integrated over time, irrespective of intake rate and
duration. Experimental exposures are adjusted to equivalent continuous exposure
levels. - •
Uncertainty factors are appropriate to adjust downward the estimated human
equivalent threshold (NOAEL) to accommodate scientific uncertainty and variability.
The normal output of a noncancer dose-response assessment is the reference dose for
ingestion and the reference concentration for inhalation. RfDs and RfCs are benchmark
concentration values that represent estimates of the acceptable daily exposure of the human
population to a specific chemical agent The RfDs and RfCs represent estimates of daily
exposure of the human population (including sensitive subgroups) that is likely to be without
an appreciable risk of deleterious effects during a lifetime (U.S. EPA, 1994c). These
reference benchmarks are based on identifying the a suitable study in which adverse effects
are observed and then dividing the NOAEL or LOAEL by the appropriate uncertainty factors.
Uncertainty factors are applied to adjust for uncertainties in extrapolating from the type
of study serving as the basis for the reference benchmark to the situation of interest for the
risk assessment Uncertainty factors are used for interspecies extrapolation, intraspecies
variability, use of less-than-lifetime data, the use of LOAEL, and incomplete studies of
potential endpoints.
A number of assumptions and extrapolations are used to derive the estimated RfDs and
RfCs, including extrapolation from animal data to equivalent human measures, use of sub-
chronic data, human variability, use of a LOAEL in the absence of an identified NOAEL, a ?
lack of data, or use of marginal-quality studies. Each of these introduces uncertainty into the
estimate. Furthermore, uncertainties may be associated with each data point resulting from
study design that does not account for bias and confounding factors, experimental error,
statistical variability, or misinterpretation of data and results.
The use of uncertainty factors (UFs) is intended to conservatively account for each of
these uncertainties in the derivation of reference benchmarks. For example, an uncertainty
factor is applied to the animal NOAEL in developing reference benchmarks (i.e., RfDs and
RfCs) because humans are assumed to be the most sensitive animal species. However, it is
possible that humans are not as sensitive as the species tested'(i.e., risk is overestimated) or
that humans are more than 10 times as-sensitive as the species tested (i.e., risk is
underestimated). Although RfDs and RfCs are considered estimates with uncertainty spanning
perhaps an order of magnitude, that uncertainty depends on whether the UFs are accurate in
describing the toxicokinetics (target organ dose) and toxicodynamics (toxic response) for each
individual chemical in both animals and humans.
Although it is generally agreed that thresholds exist, the actual threshold may vary
significantly from individual to individual based on susceptibilities. In fact, certain
August 1995 4-14
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4.0 ENDPOINTS
individuals may exhibit adverse effects even at very minute exposure levels. Given the wide
variation in individual response, a single threshold may not be representative of the
population as a whole. Individuals within a population can be expected to respond to similar
exposures differently: The uncertainty factor for interindividual variability is intended to
ensure that sensitive individuals are, protected from adverse effects. However, it is possible
that a percentage of the population may receive significantly higher doses than the reference
benchmarks and not experience adverse effects.
4.2.5.2 Carcinogens
The current approach used to estimate cancer slope factors for inhalation and oral
exposures is described in the Guidelines for Carcinogen Risk Assessment (U.S. EPA, 1987c)
and is based on the premise that there is no dose for carcinogens that presents zero risk.
Modeling procedures, typically the Linearized Multistage Model, are used to extrapolate
cancer incidence from high, experimental exposure levels to the low levels associated with the
environmental exposure to humans. The CSF is the slope at the 95 percent confidence limit
of the linear term of that extrapolated line and represents the carcinogenic potency of a
constituent A number of uncertainties associated with benchmark estimation for non-
carcinogens (e.g., relevance of animal exposures to humans, intra- and interspecies differences
in sensitivity, the temporal influence of exposure) are also relevant to the evaluation of
carcinogens. However, unlike methods used for noncarcinogens, extrapolation of cancer slope
factors is dependent on the ability of mathematical extrapolation models to represent the
carcinogenic process in humans. As the fundamental mechanistic uncertainties in the
pharmacokinetics and pharmacodynamics of cancer are better characterized, it is likely that
the uncertainties in CSF estimation will be easier to quantify. Ongoing research in the use of
distributional approaches to carcinogenic potency may provide useful insights into the
uncertainties in mathematical models, however^ the development of better mechanistic
approaches is likely to come from advances in understanding at the cellular and perhaps
genetic levels.
4.3 ECOLOGICAL BENCHMARKS
4.3.1 Conceptual Approach
There is a great deal of uncertainty associated with the development of lexicological
benchmarks for ecological receptors. Key sources of uncertainty include
•5*s»
• Selection of the appropriate level of conservatism (i.e., no effects vs. 20 percent
effects)
• Selection of enidpoints needed to infer adverse effects to populations and communities
• Selection of the most appropriate endpoints
• Interpretation of the biological significance of an effect (vs. statistical significance)
August 1995 4-15
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4.0 ENDPOINTS
• Interpretation of the entire lexicological data set (i.e., weight-of-evidence)
i
• Extrapolation from effects level to the desired benchmark level
- across speeies within the same taxonomic order
- with respect to exposure conditions (e.g., duration)
- using dose- or concentration-response information.
Specific examples of these sources of uncertainty are discussed in Section 4.3.7 on
uncertainties and issues of concern. However, three sources of uncertainty were addressed up
front in the conceptual approach to benchmark development: (1) the level of conservatism,
(2) the selection of appropriate endpoints, and (3) extrapolation to the desired lexicological
benchmark.
• Conservatism—The level of conservatism was defined in terms of the central.
premise for the ecological exit criteria: if a sufficient number of populations within
an ecosystem is protected, then the,likelihood of adverse effects that are causally
related to the chemical stressor will be reduced at the ecosystem level as well
(however one defines those adverse effects). This premise is intuitively attractive in
that protection of the whole (i.e., the ecosystem) is logically accomplished through
protection of the parts (i.e., reproducing populations; communities).* It has been
suggested that the appropriate level of conservatism for lexicological benchmarks is
the 20 percent effects level because this is the lowest level for ecological effects that
can be detected in field population analyses. Although the 20 percent effects level
may indeed be the lower limit that could be reliably confirmed in field studies, this
level reflects our current analytical anilities and not the ecological significance of the
effects level Therefore, lexicological benchmarks were generally established using a
no-effects level approach for all groups of receptors except plants. However, because
the existing data for plants are highly diverse and nonstandard (Fletcher et al., 1985;
Klaine and Lewis, 1995), the interrelation of phytoxicity data for use as regulatory
criteria is highly uncertain. Many phytotoxicity studies use concentrations, durations,
or response measurements that are inappropriate for setting criteria (Suter, 1993a).
Moreover, both algae and vascular plants vary widely in their insistence to chemicals,
and many species have been shown to be resilient and adaptive to chemical
contamination. Consequently, benchmarks for terrestrial and aquatic plants were
established using a lowest effects concentration approach as described in Sections
4.3.3 and 4.3.7, respectively.
"This analysis deals only with living ecological receptors and does not address indirect stressors such as
habitat alteration. As a result, stressors such as the reduction in dissolved oxygen due to algal bloom are not
considered in setting ecological exit criteria. In addition, other natural (e.g., parasites) or anthropogenic (e.g.,
thermal) stressors may cause ecological receptors to be more sensitive to chemical stressors. However, it was
not possible in this analysis to account for combinations of chemical and nonchetnical stressors. The Office of
Solid Waste is currently evaluating potential methods for including indirect stressors due to chemical
contamination at low levels. However, the possibility of evaluating indirect stress in a generic ecosystem is
currently beyond the state of ecological risk assessment tools.
August 1995 4-16
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4.0 ENDPOINTS
• End points—The selection of appropriate measurement endpoints was based on two
assumptions implicit in the selection of assessment endpoints described in Section 3.
First, the reproducing population is the smallest ecological unit that is persistent on a
human time scale and, therefore, is an appropriate level (as opposed to individuals)
for the development of toxicological benchmarks (U.S. EPA, 1994t). Of the potential
endpoints that were considered in this analysis, reproductive endpoints (e.g., number
of viable young per female) were clearly the most relevant and may be used to infer
a reduction in reproductive fitness in the population (U.S. EPA, 1994a). This
assumption extends to dense reproducing populations of deer mice as well as to a
breeding pair of red-tailed hawks. Although other measurement endpoints such as
developmental effects, lethality, growth, and survival were sometimes selected, the
first choice for ^measurement endpoints was reproductive effects.
Second, soil or benthic communities contain numerous species that live in intimate
contact with the; contaminated medium (and with each other). However, because of
functional redundancy and compensatory mechanisms within a community,
measurement endpoints included data on any number of species that represent the
important functional components of the community (e.g., decomposers). For
example, because species perform a variety of related functions essential to the
community, they are often thought of in terms of guilds (i.e., species that use a
common resource-in a similar way). Members of a guild may respond similarly to
toxic chemicals because they are found in the same areas, have the same diet, and
receive similar exposures (Suter, 1993a). Because there are data on so few species
that live in soils and sediments, it is highly unlikely that, for all chemicals, the most
sensitive species have been identified in soil and benthic communities. Therefore,
effects data were selected on endpoints relevant to population maintenance (e.g.,
reproductive, lethality) so that the various structural and functional niches within the
community were protected.
• Extrapolation—The third source of uncertainty addressed up front includes both
knowledge uncertainty and.model uncertainty. Consensus has not been reached on
the most appropriate methods to: (1) establish minimum data requirements for a
toxicological benchmark, (2) extrapolate from an effects levels to a no effects level,
and (3) estimate differences in interspecies sensitivity. Given the number and variety
of ecological receptors included in this analysis (predatory birds to soil infauna) as
well as the variety of effects and endpoints considered, an approach was required that
was internally consistent ands&cknowledged, at least qualitatively, the uncertainty
. involved in estimating ecological benchmarks. To this end, three categories were
established for benchmarks: adequate, provisional, and interim. These categories
were developed on a receptor-specific basis and represent a weight-of-evidence
designation for the toxicological benchmark, incorporating both the relationship of the
benchmark to the entire toxicity data set and the adequacy of the database to support .
the benchmark level. In addition, the categories recognize the dynamic nature of the
ecological risk assessment process. As the ecological database is expanded and
extrapolation methods continue to improve (e.g., population-level models;
August 1995 4-17
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4.0 ENDPOINTS
concentration-duration-response models), confidence in the appropriateness of the
benchmarks will increase.
4.3.1.1 Overview of Benchmarks for Ecological Receptors
For populations of mammals and birds, the overall approach used to establish
lexicological benchmarks was similar to the methods used to establish RfDs for humans (as
described in IRIS [ILS. EPA, 1994c]). Each method uses a hierarchy for the selection of
toxicity data (e.g., no effects levels are generally preferred to lowest effects levels) and
extrapolates from a test species to the desired benchmark. However, there are fundamental
differences in the goals of noncancer risk assessments for humans and ecological receptors.
Risk assessments of humans seek to protect the individual while risk assessments of
ecological receptors typically seek to protect populations or communities of important species.
The procedures used to develop benchmarks (i.e., RfDs) for the protection of human health
are very sensitive by design and go beyond the need to sustain the reproductive fitness in a
local population (U.S. EPA, 1992m). Consequently, benchmarks for mammals and birds were
established using three key guidelines. First, because the reproducing population was selected
as the assessment endpoint, the benchmarks were developed from measures of reproductive
success or, if unavailable, other effects that could conceivably impair the maintenance of the
population. Second, every effort was made to the taxon of the test species to the taxon of the
wildlife species. The evolutionary processes that result in obvious differences in taxa (e.g.,
morphology) also result in differences in the physiological processes that govern chemical
response. Moreover, taxonomic similarities will generally be associated with similarities in
feeding habits, physiology, and chemical sensitivity at the family classification (e.g., family
Cahidae are dog-like carnivores) and, to a le—er extent, the order classification (U.S. EPA,
199 Ih). For example, herbivores are generally more resistant to toxicants than predators
because they are exposed to plant toxins, and the enzymatic system that oxidizes natural
toxins also detoxifies pesticides and other organic chemicals (Plapp, 1981; Mullin et al., 1982,
as cited in Suter, 1993a). Third, a default safety factor of 10 was adopted only for
extrapolation from an effects level to a no effects level. A tenfold safety factor was not
applied to subchronic studies since reproductive and developmental toxicity studies are
frequently short-term. Even among target organ toxicity studies, there are many instances
where subchronic studies are actually more sensitive than chronic studies carried out on the
same substance (U.S. EPA, 1992m).
For the terrestrial plant community, the approach used to establish lexicological
benchmarks was adapted from the Effects Range Low (ER-L) approach (Long and Morgan,
1990) developed by the National Oceanographic and Atmospheric Administration (NOAA).
The NOAA ER-L approach estimates the 10th percentile of the distribution of various toxic
effects thresholds. The measurement endpoints were generally limited to growth and yield
parameters because: (1) they are the most common class of response reported in
phytotoxicity studies and, therefore, will allow for benchmark calculations for a large number
of constituents, and (2) they are ecologically significant responses both in terms of plant
populations and, by extension, the ability of producers to support higher trophic levels. It
should be noted that these benchmarks were limited to soil concentrations and do not
August 1995 4-18
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4.0 ENDPOINTS
explicitly consider the adverse impacts on plants from ambient contaminant concentrations in
the air.
For populations of fish and aquatic invertebrates, the Final Chronic Value (FCV)
developed for the Ambient Water Quality Criteria (AWQC) was preferred as the lexicological
benchmark. If an FCV was unavailable and could not be calculated from available data, a
Secondary Chronic Value (SCV) was estimated using the Tier n methods developed for water
quality criteria in the Final Water Quality Guidance for the Great Lakes System (e.g., 160 FR
15366). The SCV methodology is based on the original species data set established for the
AWQC (Stephan et aL, 1985); however, it requires fewer data points and includes statistically
derived uncertainty factors. . .
For the sediment community, the approach used to establish toxicological benchmarks
was based on methods presented in the Technical Basis for Deriving Sediment Quality
Criteria for Nonionic Organic Contaminants for the Protection ofBenthic Organisms by
Using Equilibrium Partitioning (U.S. EPA, 19931). Two key principles form the basis for the
proposed sediment quality criteria (SQQ. First, benthic species, defined as either epibenthic
or infaunal species, have a similar toxicological sensitivity to water column species. As a
result, the toxicity to benthic species can be predicted from data from water-only tests, and, in
particular, Final Chronic Values (FCV) developed for the Ambient Water Quality Criteria can
be used to represent the benthic community. Second, pore water and sediment carbon are
assumed to be in equilibrium and the concentrations are related by a partition coefficient, K^.
This assumption, described as equilibrium partitioning (EqP), provides the rationale for the
equality of water-only and sediment-only effects concentrations on a pore water basis: the
sediment-pore water equilibrium system results in the same effects as a water-only exposure.
In other words, the chemical activity is :;e same in each system at equilibrium (U.S. EPA,
19931).
For the soil community, the toxicological benchmarks were established based on
methods developed by the Dutch National Institute of Public Health and Environmental
Protection (RTVM). In brief, the RTVM approach estimates a confidence interval containing
the concentration at which the no observed effects concentration (NOEQ for p percent (95th
percentile) of the species within the community is not exceeded. A minimum data set was
established in which key functional components of the soil community (e.g., decomposer
guilds; grazing guilds) encompassing different sizes of organisms (i.e., microfauna,
mesofauna, and macrofauna) were represented.
4.3.1.2 Constituents of Ecological Concern
'*te
Although any constituent may cause adverse effects to ecological receptors, some
constituents are likely to present significant risks to wildlife at lower environmental
concentrations than for humans. For example, constituents that are highly persistent and that
bioaccumulate in the food chain may pose significantly higher risks to upper trophic level
consumers (e.g., piscivores) at acceptable exposure levels to humans. Similarly, modes of
toxic action that are unique to wildlife (e.g., eggshell thinning; stomatal closure) or exposure
August 1995 4-19
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4.0 ENDPOINTS
pathways that arc unique to wildlife (e.g., exposure via gill exchange) present risks to
ecological receptors without a human analog. Therefore, data collection activities were
concentrated on a group of constituents judged to present the most significant threats to
ecological receptors that could be adequately researched within the time frame for this
analysis. It is crucial to recognize that this list of constituents is not all-inclusive and
includes only the "short list" of chemicals judged to be the highest priority in terms of
ecological risk.
The "short list" of 47 constituents was compiled based on five characteristics under
which ecological risks may be significant even though contaminant levels in various media do
not pose significant risks to humans. The characteristics were developed after an extensive
review of the literature (e.g., U.S. EPA, 1993t; Colbom et al., 1993; Suter, 1993a) and are
functionally similar to the stressor characteristics in the problem formulation stage described
in the Framework for Ecological Risk Assessment (U.S. EPA, 1992a). In addition, available
data (e.g., Ambient Water Quality Criteria) were examined to determine how toxicity data
could be used to identify the constituents most likely to be of ecological concern. Major
stressor characteristics for chemicals that could present the highest ecological risks are:
Stressor . ' '
characteristic Description
Intensity Chemicals that bioaccumulate (and possibly biomagnify) in the food
chain present elevated exposures to certain predators
Frequency Chemicals may pc ^ considerably higher risks to ecological
receptors that are exposed continuously
Timing . Reproductive and developmental chemicals elicit adverse effects at
sensitive life stages
Scale The spatial and, especially, the temporal scale for exposure is likely
to be increased for persistent chemicals
Mode of action Chemicals may cause adverse effects to ecological receptors with no
human analog (e.g., avian reproductive effects)
Each of these characteristics was assigned an operational definition, and constituents
failing under two or more definitions were included in the group of constituents to be .
evaluated for ecological exit criteria^£or example, the frequency'characteristic was defined
in terms of the AWQC. Since aquatic'organisms live in constant contact with contaminated
water, constituents with an AWQC below the drinking water human health-based level (HBL)
were flagged'under frequency. Similarly, constituents demonstrated to decrease fecundity or
disrupt the endocrine system (the so-called estrogen mimics) were flagged under timing
(Colborn et al., 1993). Tables 4-2 and 4-3 present the lists of chemicals flagged for: (1)
having AWQC below the HBL or (2) being endocrine disruptors, respectively. A more
August 1995 4-20
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4.0 ENDPOINTS
Table 4-2._ Chemicals with Ambient Water Quality Criteria Less than Human
Health-Based Level
Hexachlorpcyclopentadiene Toxaphene
Fluoranthene Endrin
Benz(a)anthracene Benzo(a)pyrene
DDT Bis(2-ethylhexyl)phthalate
Mercury Zinc
Dimethyl phthalate Diethyl phthalate
Endbsulfan Acenaphthene
Molybdenum Cadmium
Chromium VI Copper
Selenium Butyl benzyl phthalate
Chlordane
Table 4-3. Endocrine-Disrupting Chemicals (included in first group of
constituents evaluated for ecological risk)
PCBs TCDD-2,3,7,8-
Chlordane DDT
Dieldrin Endosulfan
Lindane Parathion
Toxaphene Trichlorbphenoxyacetic acid
Cadmium Lead
Mercury • . -*» Bis(2-ethylhexyl)phthalate
Butyl benzyl phthalate Di-n-octyl phthalate
Diethyl phthalate Pentachlorophenol
Hexachlorobenzene . . .
August 1995 4-21
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4.0 ENDPOINTS
complete discussion of this screening process is presented in Appendix B along with the
ecotoxicological -profiles.
4.3.1.3 Data Collection
For each constituent of ecological concern, a thorough literature review was conducted
to identify lexicological data from laboratory and field studies relevant to the ecological
receptors evaluated in this analysis. The review focused primarily on effects likely to result
in adverse effects to populations such as fecundity, growth, and survival. However, other
effects were included to accommodate the full suite of measurement endpoints and provide a
more complete lexicological profile of the chemical (including data on bioaccumulation and
plant uptake-response slopes). The review examined secondary compilations of toxicity data
such as the Synoptic Review Series published by the U.S. Fish and Wildlife Service, the
AWQC documents, and other Federal compendia of toxicity data (e.g., HEAs, the Derivation
of Proposed Human Health and Wildlife Bioaccumulation Factors for the Great Lakes
Initiative [Stephan, 1993], Agency for Toxic Substances and Disease Registry [ATSDR]
documents, PHYTOTOX, TERRETOX, and the AQuatic toxicity Information REtrieval
database [AQUIRE] [AQUIRE, 1994]). Toxicity data on soil organisms were obtained for
several constituents from van de Meent et al. (1990). In addition, on-line literature searches
were conducted to identify primary sources of toxicity data on constituents lacking sufficient
data in the secondary sources. Primary studies were reviewed and other studies were
identified in conventional literature reviews.
Based on this information, a master table was developed for each constituent that
contained: (1) reproductive/developmental enects or other adverse effects that could
potentially impact the ability of populations to survive and reproduce, (2) chronic and
subchronic effects levels, (3) acute toxicity, and (4) frank effects levels (FELs). For each
constituent, a lexicological profile was developed that describes the data and rationale used in
the benchmark selection. The profile describes the benchmark derivation and includes the
master table, a summary table for the generic aquatic and terrestrial ecosystem, and summary
table of bioaccumulation and biconcentration factors.
Tables 4-4 and 4-5 present the ecotoxicological benchmarks for 47 constituents of
ecological concern for representative species in the freshwater ecosystem (including sediment
communities) and the terrestrial ecosystem (including soil communities), respectively. The
derivation of these benchmarks is described below for each group of ecological receptors.
-*» . • •
4.3.2 Mammals and Birds ' •
Adverse effects levels were identified on endpoints ranging from reproductive to target
organ studies (e.g., liver pathology). The inteni of ihe data-gathering effort was to create a
stressor-response profile illustrating ihe range of effecis and effects levels for each of the
constituents of concern. However, since the reproducing population was considered the
assessment endpoint, data-gathering efforts were focused primarily on reproductive effects and
August 1995 4-22
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Table 4-4. Toxicological Benchmarks for Ecological Receptors in the Freshwater Ecosystem
Mammals (mg/kg-d)
ConatHuant name
Acenaphihene
Aldrln
Antimony
ArierdcV
Barium
Benz(a)anthracene (1,2-)
Benzo(a)pyrene
Beryllium
Bis(2-ethylhexyl)phthatate
(also OEHP)
Butytberuyl phlhalate
Cadmium
Chlordana
Chromium VI
Chrysene
Copper
DOT
Ol-n-octyl phthalate
DleWrln
Olathyl phlhalata
Dimethyl phthalate
Endosullan
Endrln
Fluoranlhene
Heptachlor
Heptachlor epoxkte
Hexachlorobenzane
Mink
ID
6.28-02
1.38-01
3.9e+oo
ID
ID
4.46-01
'tl-
3.2*401
1.76+02
8.28-01
1.96+00
1.26+00
ID
1.8»+00
3.7e-01
ID
1.06-02
ID
ID
2.96+00
2.38-01
ID
3.3«-01
ID
1.26+00
River
otter
ID
3.16-02 p
7.0e-02 a
2.49+00 •*
ID
ID
2.66-01 p
ID
I.Be+01 a
0.2e+01 a
4.96-01 a
1.18+00 a*
7.46-01 p*
ID
1 .Oe+00 a
2.0e-01 a*
ID
e.pe-03 a
ID"
ID .
1.86+00 a*
1.38-01 a
ID
2.06-01 a
ID
7.16-01 a*
Bird* (mg/kg-d) . '
Bald
eagle
ID
4.06-03
ID
3.98+00
4.16+01
ID
ID
ID
ID
ID
1.46+00
ID
ID
ID
ID
2.0a-02
ID
4.06-02
ID
ID
10
2.08-02
ID
ID
ID
2.6e01
Oeprey
. ID
5.06-O3
ID
6.oa+oo
5.28+01
ID
ID
ID
ID
ID
1.76+00
ID
ID
ID
ID
2.0e-02
ID
4.06-02
ID
ID
ID
3.06-02
ID
ID
ID
3.26-01
Great
blue
heron
ID
5.06-03
ID
4.76+00
4.96+01
ID
ID
ID
ID
ID
1.66+00
ID
ID
ID
ID
2.06-02
ID
4.06-02
K>
ID
ID
2.08-02
10
ID
ID
2.96-01
Lesser
Mallard Scaup
ID 10
6.06-03 6.06-03
ID ID
5.56+00 ' 6.26+00
5.88+01 6.46+01
ID ID
ID ID
ID ID
ID ID
ID ID
1.96+00 2,16+00
ID ID
ID 10
ID ID
ID ID
2.06-02 2.0e-02
ID ID
5.0602 5.06-02
10 ID
ID ID
ID .ID
3.00-02 3.06-02
ID ID
ID ID
ID ID
3.46-01 3.88-01
Spotted
King- 'send- Ksrring
Usher piper gull
ID 10 ID
9.08-03 1.36-02 6.08-03 p*
ID ID ID
9.28+00 1.26+01 5.88+00 a
9.66+01 1.36+02 6.06+01 a
ID ID ID
ID ID ID
ID - ID ID
ID ID ' ID
ID ID ID
3.26+00 4.36+00 1. 96+00 a
ID ID ID
ID ID ID
ID ID ID "
ID 10 ID
4.08-02 5.08-02 2.06-02 p
ID ID ID
8.0602 l.le-01 5.08-02 a
ID 10 ID
ID ID ID
ID ID 10
5.08-02 6.06-02 3.08-02 a
10 10 ID
ID 10 10
ID ID ID
5.88-01 7.98-01 .3.58-01 1
Fleh/
aquatic
Invert.
(mgA.)
2.36-02 a
1.8805 I
3.06-02 a
8.16-03 . a
1.06+00 I
2.56-02 1
1.38-05 1
5.16-03 I
5.56+00 I'
I.Be+01 I
1.16-03 a*
1.76-04 a
1.16-02 a*
ID
1.26-02 a*
1. 36-05 1
ID
6.36-05 a
256-01 I
1.46+02 1
S.ea-05 a
6.16-OS a
6.26-03 a
6.96-03 1
5.16-01 1
6.06-03 a
Benthlc
community
(mg/kg
aedlment)
9.06+00
2.26+00 '
. a
1
ID
ID
10
4.98-01
6.78-01
1
1
ID
5.88+03
1.78+01
1
1
ID
5.98+00
a
ID
ID
ID
4.18+00
1
10
6.46-01
2.28+00
2.98-01
7.48-03
3.98-01
'3.68+01
2.98-02
1.28+00
7.78+01
a
1
1
a
a
a
1
1
a
Aquatic
plants
(MBA)
5.28+02 1
ID
6.18+02 1
4.88+01 1
ID
ID
ID
1. Oa+05 1
ID
ID
2.06+00 1
ID
2.08+00 1
ID
1.06+00 I
3.06-01 1
ID
ID
8.68+04 1
ID
10
ID
5,48+04 1
2.76+01 I
ID
ID
o
2
(continued)
-------
Table 4-4 (continued)
Mammals (mg/kg-d)
. Constituent name
Hexachtofocyclo-
oentadiene
Hexachkxocyclohexane,
gamma- (Undane)
Hexachkxophene
Kepone
Lead
Mercury
Methoxychtof
Methyl parathlon
Molybdenum
Nickel
Parathlon
Penlachkxobenzene
Pentachtorophenol
•Polychlorinated biphenyls
(Arodo>-1254)
Selenium
Silver
TCOD. 2.3,7.8-
Toxaphene
Trichlorophenoxyacetlc
add. 2.4.5-
Vanadium
Zinc
Mink
3.96+01
ID
1.46+00
6.08-01
3.26-O3
3.06-01
7.2f+$
1 .78+00
7.68-02
3.36+01
4.40-01
3.68+00
3.08+00
1.66-01
3.06-02
ID
4.36-07
1.26+00
2.86+01
3.56-01
1.48+02
River
otter
2.26+01
ID
7.66-01
3.66-01
9.06-03
1.76-01
4.06+01 .
1.00+00
4.18-02
2.06+01
2.76-01
2.08+00
1.88+00
9.06-02
2.08-02
ID
2.66-07
6.96-01
1. 68+01
2.28-01
8.06+01
a*
•'
•
a*
•'
a
a
a
a*
a*
P
a
a
a
a
P4
a
P
*
Girds (mg/kg-d)
Bsld
eagle Osprey
ID ID
5.48-01 6.88-01
ID ID
416+00 5.08+00
5.96-03 7.66-03
S.Oe-03 6.08-03
ID ID
3.56-01 4.36-01
ID ID
ID ID
1.26+00 1.58+00
ID ID
1.66+00 4.16+01
1.28-01 1.66-01
7.36-01 9.06-01
ID ID
1.46-05 1.26-05
3.06-02 3.08-02
ID ID
1.16+00 1.36+00
ID ID
Greet
blue
heron
ID
6.58-01
ID
4.68+00
7.16-03
5.06-03
ID
3.9e-01
ID
ID
1.56+00
ID
3.76+01
1.66-01
8.26-01
ID
1.16-05
3.08-02
ID
. 1.16+00
ID
Mallard
ID
7.76-01
ID
5.46+00
8.46-03
6.06-03
ID
4.66-01
ID
ID
1.76+00
ID
4.46+01
1.86-01
9.88-01
ID
1.3B-05
4.08-02
ID
1.36+00
ID
Spotted
Lesser King- sand- Herring
Scaup fisher piper gull .
ID ID ID ID
8.56-01 1.38+00 1.76+00 7.96-01 a
ID . ID ID ID
6.18+00 9.16+00 1.26+01 5.78+00 a
9.48-03 1.46-02 1.98-02 8.76-03 p
7.06-03 1.18-02 1. 56-02 7.06-03 a
ID ID ID ID
6.26-01 7.66-01 1.16+00 4.76-01 a
ID ID ID ID
ID ID ID ID
1.9e+00 2.90+00 3.86+00 1.86+00 1
ID ID ID ID
4.96+01 7.46+01 1.06+02 4.56+01 I
1.9e-01 2.9e-01 3.9e-0t 1.80-01 p
l.le+00 1.66+00 2.28+00 .9.9e-01 a
ID ID ID ID
1.56-05 2.26-05 2.98-05 1.48-05 I
4.08-02 6.08-02 8.08-02 4.06-02 a
ID , ID ID ID
1.58+00 2.36+00 3.26+00 1.3e+00 a
ID ID ID ID
Fish/
aquatic
Invert.
(mg/L)
7.56-04
8.06-05
I
a
ID
3.68-04
3.2eO3
1.36-03
3.06-05
3.26-05
I
a
I'
a
1
2.46-01 1
1.66-01
1.36-05
1.66+00
1.36-02
2.06-05
5.0803
3.66-04
a*
a
1
a*
1
a
1
ID
1.36-05
a
t.Oe-02 1
1.96-02
1.16-01
1
a*
Benthlc
community
(mgVkg
sediment) ,
2.56+00 1
3.46-01 a
ID
7.38-02 I
ID
ID
4.3a-02 a
I.Oe-03 1
ID
ID
3.66-03 a
8.18+00 i
ID
1.66+00 1
ID
ID
ID
5,46-02 a
6.26-01 1
ID
ID
Aquatic
plants
(ug/L)
ID
S.Oe+02 1
ID
ID
5.00+02 1
5.06+00 1
ID
ID
ID
5.06+00 1
ID
ID
ID
1 .Oe-01 |
1.00+02 I
3.06+01 1
ID
ID
ID
ID
S.Oe+01 I
o
M
•T3
O
S
a - Adequate.
I • Interim.
ID - Insufficient data.
p - Provisional.
' • Adverse effects may occur below the benchmark level.
Haxachtoroberuene (flsh/daphnid)—From AWQC the available data Indicate that HCB does not causa significant
adverse effects ol freshwater aquatic life at or below 6 ug/L."
-------
Table 4-5. Toxicological Benchmarks for Ecological Receptors in the Terrestrial Ecosystem
Mammal* (mg/kg-d)
Constituent name
Acanaphthene
Aldtin
Antimony
Artente
Barium
Benz(a)anthrac«ne
(1.2-)
Benzo(a)pyrene
Beryllium
Bi»(2-ethylhexyl)-
phthalata (alto
OEHP)
Butylbeazyl phthalate
Cadmium
Chtordane
Chfomlum VI
Chrysena
Copper
DOT
Dl-n octyt phlhalate
Dleldfin
Olethyl phthalate
Dimethyl phthalats
EndosuKan
Endrin
Fluor antrjane
HeptacNor
HeptacMor epoxlde
Hexachkxobanzene
Short-
tailed Deer Meadow
shrew mouee vole
10 ID 10
1.4001 1.48-01 1.28-01
3.26-01 3.18-01 2.68-01
l.le+01 1.1e+01 9.38+00
ID ID ID
ID ID ID
1.28+00 1.28+00 1 .Oe+00
ID (D ID
eie+bi p.$+6i eJe+oi
4.28+02 4.18+02 3.48+02
2.38+00 2.28+00 1.98+00
5.26+00 5.1B+00 4.40+00
3.58+00 3.40+00 2.80+00
ID ID ID
4.78+00 4.60+00 3.80+00
9.38-01 9.00-01 7.50-01
ID ID- ID
2.98-02 2.88-02 2.58-02
ID 'ID ID
ID ID ID
8.78+00 8.48+00 6.9e+00
S.ee-01 5.68-01 4.76-01
ID ID ID
0.1e-01 8.9o01 7.70-01
ID ID ID
3.3a+00 3.20+00 2.8O+00
Easiam
cottontail
ID
4.96-02
1.1e-01
3.Be+00
ID
ID
4.2801
ID
2.80+01
1.46+02
7.88-01
1.86+00
1.26+00
ID
1.60+00
3.2e-01
ID
1.0e-02
ID
ID
3.1e+00
2.08-01
ID
3.1e-01
'0
1.U+00
Red
. fox Racoon
ID ID
3.68-02 3.48-02
B.08-02 8.08-02
2.78*00 2.68+00
ID ID
ID ID
S.Oe-01 2.90-01
ID ID
2.16+01 2.00+01
1.18+02 1.08+02
6.68-01 6.36-01
1.38+00 1.28+00
8.58-01 8.18-01
ID ID
1.28+00 1.88+00
2.48-01 2.38-01
ID ID
7.08-03 7.08-03
ID ID
ID ID
2.18+00 2.00+00
1.5e-01 1148-01
ID 10
2.3001 2.18-01
ID ID
8.18-01 7.78-01
Whrte-
talled
deer
ID
1.7e-02 p
4.08-02 a
1.30+00 a*
ID
ID
1.50-01 p
ID
1.0e+01 a
5.U 01 a
2.78-01 a
6.28-01 a*
3.88-01 p'
ID
5.78-01 a
l.la-01 a*
ID
3.08-03 a
10
ID
9.7a01 a*
7.08-02 a
ID
l.la-01 a
10
3.99-01 a*
•Bird* (mg/kg-d)
Red-
tailed
hawk
io
6.08-03
IP
5.58+00
5.78+01
ID
ID
ID
ID
ID
1.98+00
ID
ID
ID
ID
2.08-02
ID
S.Oe-02
ID
. 10
ID
3.08-02
10
ID
ID
3.5e-01
American
kestrel
ID
1.08-02
ID
9.68+00
1.08+02
ID
ID
IP
ID
ID
3.48+00
ID
ID
ID
ID
4.08-02
ID
8.0e02
ID
ID
ID
5.0002
ID
ID
ID
6.1e01
Northern
bobwhlle
ID
9,0e-03
ID
8.98+00
9.38+01
ID
ID
ID
ID
ID
3.68+00
ID
10
ID
ID
3.08-02
10
8.08-02
ID
ID
ID
4.08-02
ID
ID
ID
5.58-01
American
robin
ID
l.la-02
ID
l.le+01
1.16+02
10
10
ID
ID
10
3.78+00
ID
ID
ID
ID
4.08-02
ID
9.06-02
ID
ID
10
S.Oe-02
ID
10
ID
6.78-01
American
woodcock
ID
9.08-03 p'
ID
8.58+00 a
8.98+01 •
10
ID
ID
ID
ID
3.18+00 a
ID
ID
ID
ID
3.08-02 p
ID
8.08-02 •
ID
10
ID.
4.08-02 a
ID
ID
ID
5.68-01 1
Soil
community
(mg/kg eoll)
ID
ID ,
ID
ID
ID
ID
ID
ID
. ID
ID
6.98-01 p
ID
ID
10
1.38+00 p
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
Terrestrial
plants
(mg/kg soil)
1 ID
1 ID
5.06+00 I
1.08+01 1
5.08+02 1
To
ID
1 .06+01 I
ID
ID
3.08+00 p
ID
1.88+00 1
ID
1.08+02 1
ID
ID
ID
ID
ID
ID
10
ID
ID
ID
ID
o
w
3
2
H
01
(continued)
-------
Table 4-5 (continued)
Mammals (mg/kg-d)
Constituent name
HexaohlorocydD-
pentadlene
Hexachkvocyclo-
hexane, gamma-
(Llndane)
Hexachlorophene
Kepone
Lead
Mercury
Methoxychlor
Methyl parathlon
Molybdenum
Nickel
Parathlon
Pentachkxobenz ene
Pentachkxophenot
Polychlorinated
biphenyls
(Arodor-1254)
Selenium
Silver
TCDD. 2.3.7.8-
Toxaphene
Trlchlorophenoxy-
acette add. 2.4,5-
Vanadlum
Zinc ,
Short-
tslled Deer Uesdow
shrew mouse vote
0.00+01 9.68+01 8.00401
ID ID ID
3.40400 3.30400 2.8e+00
1.79+00 I.Ba+OO 1.4e+00
0.66-03 9.2e-03 7.6e-03
7.7e-01 7.5e-01 6.20-01
1.88402 1,8t»02 1.58+02
4.9e+00 4:7d+00 4.16400
1.94-01 1.9601 1.58-01
9.36+01 9.06+01 7.98+01
1.29+00 1.26+00 1.18+00
9.18+00 8.96+00 7.48+00
8.46+00 8.28+00 7.16+00
4.18-01 4.08-01 3.4601
8.06-02 8.06-02 7.08-02
ID ID ID
1.26-06 1.28-08 9.28-07
3.28+00 3.18400 2.68+00
. 7.08+00 688+01 6.78+01
0.96-01 9.68.-01 8.08-01
3.68402 3.5*402 2.06402
Eastern
cottontail
3.46401
ID
1.28+00
5.78-01
3.46-03
2.60-01
6.36+01
1.78+00
6.5602
3.28+01
4.2e-01
3.18+00
2.98400
1.46-01
3.06-02
ID
4.16-07
1.1*400
2.46401
3.46-01
1.26402
Red
fox Racoon
2.50401 2.40401
ID ID
8.78-01 8.48-01
4.10-01 3.00-01
2.30-03 2.28-03
2.06-01 1.00-01
4.68+03 3.48402
1.26400 1.16400
4.06-02 4.76-02
2.36401 2.26+01
3.16-01 2.06-01
2.38+00 2.26400
2.10400 2.06+00
1.18-01 1.06-01
2.06-02 2.06-02
ID ID
2.06-07 2.80-07
8.00-01 7.70-01
1.80401 1.70401
2.50-01 2.40-01
0.20401 8.06401
Whtte-
talled
de*r
1.20401 a*
ID
4.20-01 a*
2.00-01 a
l.la-03 a*
0.00-02 a*
2.26+01 a
5.88-01 a
2.3e-02 a
1.10* 1 a*
I.Se-61 a*
1.18+00 p
1.08+00 a
5.06-02 a
1 .Oe-02 a
ID
1.4a-07 a
3.00-01 p*
8.60400 a
1.20-01 p
4.40401 a
Birds (mg/kg-d)
Red-
tailed
hawk
ID
7.50-01
ID
5.50400
8.30-03
6.00-03
ID
4.76-01
.ID
ID
1.76400
American
kestrel
ID
1.36+00
ID
0.60400
1.46-02
1.16-02
ID
8.26-01
ID
ID
3,08400
Northern
bobwhll*
• ID
1.20400
ID
8.76400
1.36-02
1.06-02
ID
7.5e-01
ID
ID .
2.86+00
American
robin
ID
1.56400
ID
1.16401
1.66-02
1.26-02
ID
0.06-01
ID
ID
3.38400
American
^*rtrntftf*rk
WOOQCOCK
ID
l.2e+oo a
ID
8.80400 a
1.30-02 p
1.00-02 a
ID
7.50-01 a
ID
ID
2.60400 1
4.56401
1.78-01
O.Be-01
ID
1.36-05
4.06-02
ID
1.46400
ID
7.86401
3.08-01
1.78400
ID
2.36-05
6.00-02
ID
2.40400
ID
7.16+01
2.80-01
I.Sa+00
ID
2.16-05
6.06-02
ID
2.26400
ID
8.66401
3.46-01
1.08+00
ID
2.66-05
7.06-02
ID
2.66400
. ID
726401 1
2.76-01 p
1.60400 a
ID
2.08-05 1
5.00-02 a
ID
2.30400 a
ID
Soil
community
(trig/kg soil)
ID
ID '•
ID
ID
2.S0-01 p
0.40-01 p
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
3.60-02 p
Terrestrial
planta
(mg/kg eoll)
I ID
l
.ID
ID
ID
5.06401 p
3.06-01 p
ID
ID
ID I
3.06+01 p
ID
ID
ID
4.06+01 I*
1.08+00 p
2.06+00 I
ID
ID
ID
2.06400 I
. 5.00401 p
13
O
2
H
a - Adequate.
I .Interim.
ID - Insufficient data.
p - Provisional.
* . Adverse effects may occur below the benchmark level.
Hexachloroberuene (fisWdaphnld)—From AWQC the available data Indicate that HCB does not cause significant
adverse •fleets of freshwater aquatic Hie at or below 6 (io/L."
-------
4.0 ENDPOINTS
other effects reasonably expected to impair the ability of a population to maintain itself (e:g.,
developmental, mortality, growth, survival).
Once the database was assembled, the data were evaluated to select the most
appropriate study with which to establish the toxicological benchmark. Although no
precedent existed for establishing an adequate toxicological data set for mammals or birds, a
minimum data set was adopted for each Class to be consistent with other efforts by EPA.
The minimum data set included: (1) evidence of reproductive, developmental, and growth/
survival effects; (2) toxicity data on at least three species (preferably in more than one order);
and (3) dose-response data for at least one study. During this data evaluation process, criteria
were developed to simplify the study selection process and provide the framework for a
weight-of-evidence approach to the entire data set For convenience, no adverse observed
effects levels and no observed effects levels are covered under one term—no effects levels
(NELs)—and lowest observed adverse effects levels and lowest observed effects levels are
covered under another term—lowest effects levels (LELs)."1 The criteria included the
following:
• Studies on oral exposure (e.g., dietary, gavage) were always preferred to studies on
other exposure routes (e.g., subcutaneous, intraperitoneal) unless all of the following
conditions were met: (1) sufficient information was available to extrapolate to an
oral administration (i.e., pharmacokinetic data), (2) the endpoint was relevant to
reproduction, and (3) the study contained at least three data points on a dose-response
curve.
• Studies on reproductive and developmental effects were preferred to other studies
(e.g., growth, mortality) since the assessment endpoint was the reproducing
population. Other endpoints believed to be relevant to maintaining wildlife
populations included mortality, growth, and survival. However, mortality is not
always related to the response of the population to a strcssor and frequently occurs at
significantly higher concentrations than do other adverse effects (including
reproductive effects). Simlarly, it is difficult to establish a causal relationship
between growth and survival endpoints and population impacts because of natural
population variability.
• Studies providing an NEL based on at least three doses were preferred to studies
providing an LEL. The identification of the NEL depends on the dosage spacing
selected by the investigator a^nd, in studies in which two doses are administered, this
. spacing is particularly difficult to delineate. Although the LEL represents a
contaminant concentration at which adverse effects occur, it is a level associated with
•Adverse effects levels, or AELs, consist of NOAELs and LOAELs and .have different toxicological.
significance than effects levels, or Bus, that consist of NOELs and LOELs. Grouping these levels under NELs
or T.F.T.< was done to provide less cumbersome terminology in the text Where appropriate, Ihe toxicological
profiles (Appendix B) discuss the selection of AELs vs. ELs.
August 1995 4-27
-------
4.0 ENDPOINTS
minimal uncertainty and, therefore, preferrable to an NEL with fewer than three
doses.
• In general, the lowest value (i.e., among NELs and LELs) was selected as the
benchmark study provided that the study evaluated reproductive endpoints or other
endpoints presumed to impair the ability of the population to sustain itself (e.g.,
growth) and contained dose-response information (i.e., not a single-dose NEL).
• Ah NEL with dose-response information on a representative species or taxonomically
similar species was preferred to an NEL on a species from more distant taxon. This
criterion was also applied to the LEL. In addition, study species were used only if
they were in the same class as the representative species (i.e., no extrapolation from
mammals to birds).
From these criteria, it is obvious that all possible permutations of toxicity data were not
covered. For instance, the criteria did not provide specific rationale for selecting the
benchmark study when an NEL for neurological effects was several orders of magnitude
below an NEL for reproductive effects, assuming adequate dose-reponse information in both
studies. Although it is apparent that using a benchmark several orders of magnitude, above
the NEL may result in a high incidence of neurological effects in a given population, it is not
known how this effect will manifest itself at the population level. Indeed, it is not known
how most sublethal effects, exclusive of reproductive endpoints, are manifested at the
population level (e.g., Hallam et al., 1993; U.S. EPA, 1993t).
To address these concerns, the effects levels selected for mammals and birds were
assigned to one of three categories: adequate, provisional, and interim. These categories
reflect the adequacy of the data set to develop benchmarks and the nature of the effects level
selected for benchmark derivation (e.g., NOAEL vs LOAEL). The categories were defined as
follows:
• Adequate—The study value selected was an NEL based on a reproductive,
developmental, growth, or survival endpoint that was lower than any other NEL or
LEL for these endpoints; In addition, the data set contained studies conducted over
chronic or subchronic durations, or during a sensitive life stage for the three types of
endpoints relevant to population viability (i.e., reproductive, developmental effects,
and growth and survival effects).
--*»
• Provisional—The study value "selected was an LEL/10 (LEL divided by a LOAEL-
to-NOAEL safety factor of 10) on a reproductive, developmental, or growth/survival
endpoint that was lower than any other NEL or LEL for these endpoints. In addition,
the data set contained studies conducted over chronic or subchronic durations, or
during a sensitive life stage for the three types of endpoints relevant to population
sustainability.
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• Interim — The study value selected was the lowest NEL or LEL/10 on a reproductive,
developmental, or growth/survival endpoint This category did not require studies on
the entire suite of endpoints for population sustainability (i.e., reproductive,
developmental- effects, and growth and survival effects).
For data sets in which the benchmark was an order of magnitude or more above the NEL or
LEL/10 for other adverse effects, an "*" was placed next to the category designation to
indicate that adverse effects may occur at the benchmark level.
The NEL (or LEL/10) was then scaled to the representative species using a cross-
species scaling algorithm adapted from Opresko et al. (1994) for NOAEL extrapolation from
test species to wildlife. The methodology is based on allometry demonstrating that (1)
numerous physiological parameters (e.g., metabolic rates) are related to body size and (2)
smaller animals have higher metabolic rates and, therefore, tend to be more resistent to toxic
chemicals due to rapid rates of detoxification and excretion.* The algorithm is identical to
that proposed by EPA for carcinogen risk assessment in which animal data are adjusted to an
equivalent human dose in relation to body surface area (57 FR 24152-24173).
For lack of direct measurements, the body surface area can be expressed in terms of
body weight raised to the 3/4 power; Mathematically, the dose (d) per unit surface area (D)
is given by:
Because the pharmacokinetic dose has been demonstrated to be roughly equivalent across
species when the dose is scaled per unit body surface area, the dose to wildlife species "w" is
equivalent to test species "t":
dw x Kl/4 - *, x K1'4 - - (4-2)
Rearranging to solve for the dose to the wildlife species (d^ gives:
"The scaling algorithm was not applied to chemicals known to be metabolized into more toxic daughter
compounds. In addition, interspecies scaling was not conducted across species if other data suggested species-
specific differences in sensitivity not explained by differences in body size.
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1/4
= dt x L_ = dtx (bwt I MvJ1/4 . (4-3)
Substituting the NEL (or LEL/10) for the wildlife and test doses, respectively, yields the
following equation used to derive the lexicological benchmark for wildlife:
Benchmark^ = NELt x (bwt / bwj114 . (4-4)
Where appropriate, body weights for specific genders were used in the scaling equation. For
example, if a reproductive study was used in which only females were exposed to a toxicant,
then female body weights were used for both the test species and the wildlife species.
' Finally, the scaled benchmark was evaluated within the context of the data set using a
weight-of-evidence approach. The species-specific data were compared to determine if the
data set supported the dose scaling because, in some cases, size may not be the primary
determinant of the toxic response. For example, scaling a dioxin dose from a hamster Qeast
sensitive) to a guinea pig (most sensitive) would produce a guinea pig benchmark that
contradicts empirical toxicity data. Therefore, if the data suggested that scaled benchmarks
were inconsistent with the toxic responses observed in wildlife and laboratory species,
allometric scaling was not used. Wherever possible, a toxicity equivalent approach was used
to estimate an interspecies extrapolation factor. The resulting extrapolation factor was applied
to the NEL (or LEL/10) only if supported by the weight-of-evidence for the entire data set If
an extrapolation factor could not be estimated, the scaled NEL (or LEL/10) was used as the
benchmark.
4.33 Terrestrial Plants
Adverse effects levels for terrestrial vascular plants were identified for endpoints
ranging from percent yield to root length. Data collection efforts were focused on growth
(e.g., seed germination, seedling) and yield because (1) a substantial body of data exists on
these endpoints and (2) these endpoints are highly relevant to plant population sustainability
(Will and Suter, 1994). In addition, growth and yield are highly relevant ecological responses
in terms of the ability of plant biota to support higher trophic levels. However, in view of the
diversity of soils, plant species, chemical forms, and test procedures, it was not possible to
derive a benchmark from a single stv^y to predict effects on generic plant communities. For
example, very few constituents have toxicity data on a sufficient number of species to
represent even a simple plant community including short-lived and long-lived plants, flower-
ing and nonflowering plants, high seed producers, and plants with extensive root systems
(Eijsackers, 1994). Given the deficiencies in the phytotoxicity database, the Effects Range
Low (ER-L) approach used in Will and Suter (1994) was adopted for this analysis. The ER-L
approach was developed by the National Oceanographic and Atmospheric Adminstration to
estimate sediment screening benchmarks for the U.S. EPA Region 4 (Long and Morgan,
1990); The ER-L estimates the 10th percentile concentration from a range of lowest observed
August 1995 ...... •' " rirmrrrmnmrriny""""' .......... '" ..... """""""""" ....... """r""' " •Trr"TrrrTTrmr ;" .......... ....... .....
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effect concentrations (LOECs) for a minimum data set of 10 studies. However, because few
data were available.for most constituents, the minimum data set for plants consisted of one
soil-based LOEC that contained concentration-response information.
The primary source of effects data on plants was the Toxicological Benchmarks for
Screening Potential Contaminants of Concern for Effects on Terrestrial Plants: 1994 Revision
(Will and Suter, 1994), a report prepared by the Oak Ridge National Laboratory based on the
..PHYTOTOX database. This report summarizes available phytoxicity data in PHYTOTOX for
the endpoints listed above and includes exposure duration (when available) on LOEC and
NOEC values. Since the benchmarks were based on a single data source, relatively simple
criteria were established for study selection: .
• Only soil-based studies that reported concentration-response data were considered
suitable for benchmark derivation.
• Studies reporting both LOEC and NOEC values were preferred to studies that did not
report a NOEC.
• The 10th percentile of the LOEC values was estimated if the data set contained a
minimum of 10 values. If there were fewer than 10 studies, the lowest LOEC was
selected as the plant benchmark.
• If data were unavailable in Will and Suter (1994) or PHYTOTOX, other data sources
listed above were reviewed. Origin'1, studies were obtained and benchmarks were
derived using the ER-L method or by selecting the lowest LOEC for which
concentration-response data were available.
To reflect deficiencies in the plant toxicity database, the benchmark concentrations for
terrestrial plants were assigned to one of the three categories defined below:
• Adequate—No benchmarks were assigned to this category. At present, the
phytotoxicity database is very limited and the Agency has not proposed standard
protocols to develop.toxicological benchmarks for plants. At a minimum, further
research is needed on: (1) quantifying the impact of soil characteristics on
phytotoxicity, (2) identifying endpoints with high biological significance to plant
physiology and toxic response, and (3) selecting species and testing methods (e.g.,
duration of exposure) to forTS** core requirement for phytotoxicity benchmarks.
• Provisional—The benchmark was the 10th percentile of study LOECs that met the
criteria described above.
• Interim—The benchmark selected was the lowest LOEC presented in Will and Suter
(1994) or identified in the open literature.
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4.3.4 Soil Community
Data on adverse effects levels were collected for the taxa identified in Section 3 to
develop community-level benchmarks for soil fauna. The adverse effects levels for soil
species included endpoints on reproduction, growth, mortality, mobility, sexual development,
population increase/decrease, and regeneration. Data on reproductive endpoints were
considered particularly important because of the ecological importance of reproductive
maintenance of soil organisms (van Straalen and Denneman, 1989). Toxicity data were
identified for four categories of exposure: (1) topical application; (2) surface-soil
application, in which the soil organisms are placed onto a treated surface; (3) mixed-soil
application, in which the soil organisms are placed into a soil that was mixed with a
constituent; and (4) food application (i.e., chemical mixed with organic food source).
The following criteria were established to develop an appropriate data set for the
"typical" soil community species:
• NOECs were generally preferred to LOECs, however, the geometric mean of a
NOEC and LOEC was calculated if the effects concentrations were from the same
study. The geometric mean of NOEC values was calculated for individual species in
each taxa for similar endpoints. For each of the eight taxa representing the soil
community, the lowest value that was a: (1) NOEC, (2) NOEC/LOEC geometric
mean, or (3) species-specific geometric mean for similar endpoints was used to
represent species in that taxa.
• Field studies were generally preferred to laboratory studies. This criterion reflects the
belief that studies conducted in the field will provide a more realistic representation
of soil toxicity since they occur under "natural" conditions; i.e., the mitigating effects
of rain, sunlight, etc., are present.
• For each taxa, studies were preferred that matched the field route of exposure most
closely. For example, organisms (e.g., arthropods) that dwell on the soil surface or in
the leaf-litter layer, are likely to be exposed through direct contact with the top
surface layer. Therefore, surface-soil application is an appropriate study type for
. arthropods.
• Studies were preferred that contained information on the percent clay and organic
matter of the soil media. Whsg information on these parameters was available, it was
used to adjust the toxicity of die compound using the procedure of Slooff (1992) and
van Straalen and Denneman (1989).*
*In cases where feeding studies were used, the percent organic matter was assumed to be 95 percent and the
percent clay was assumed to be zero (van de Meent et al., 1990).
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For organic contaminants, the raw NOEC values for the representative soil species were
normalized for the fraction of organic carbon present in the experimental soil according to
Equation 4-5:
. . NOEC f'
NOEC1 = x
' OC
where NOEC is the NOEC normalized for organic carbon, f ^ is the fraction of organic
carbon in the "standard" soil, typically about 6 percent (Slooff, 1992), and f^ is the fraction
of organic carbon in the experimental soil. The fraction of organic carbon in a soil is equal
to the fraction of organic matter (fom) divided by 1.724 (f^ = fon/1.724). Normalizing to a
standard soil is analogous to the adjustment of aquatic toxicity data for water hardness or pH
and accounts for variability in selected soil characteristics that affect toxicity (Stephan et ad.,
1985).
For metals, the raw NOEC values were adjusted for the percent clay present as well as
the fraction of organic matter. Experimental results for metal contaminants were normalized
with as follows:
(4.6)
where R' is the reference value for the standard soil with / percent clay and h percent organic
matter, and R is the experimental value corresponding to given levels of clay (/) and organic
matter (h), as described by Slooff (1992).
After the data set was completed for the representative soil species, the soil community
benchmarks were determined using the RTVM effects assessment methodology (Aldenberg
and Slob, 1993; Slooff, 1992; van Straalen and Denneman, 1989). The RIVM methodology
consists of two steps: (1) fitting a distribution to the log of the selected endpoints, and (2)
extrapolating to a benchmark concentration based on the mean and standard deviation of a set
of endpoints. Using the NOEC data for each category of soil invertebrates, the distribution is
used to estimate a high-end (95th percentile) and central tendency (50th percentile)
contaminant concentration in soil which will not adversely affect the soil community.
The RIVM methodology assumes that the logs of the endpoint data are independent
random trials from a logistic distribution (Kooijman, 1987).* The logistic distribution is
similar to the more familiar normal distribution in that it is bell shaped and is centered about
its mean. The logistic distribution, however, allows for a larger number of values further
from the mean then would a normal distribution with the same mean and standard deviation.
Because the tails of the logistic distribution are more extended than the normal distribution,
using a logistic distribution is conservative than using the normal distribution.
*The procedure used to calculate AWQC assumes a triangular distribution of the response data..
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A hazardous concentration (HCp) such that the NOEC for 100-p percent of the species
within an ecosystem is not exceeded was calculated following the procedures in van Straalen
and Denneman (1989) and Aldenberg and Slob (1993). The RTVM methodology assumes
that, at concentrations above the HCp, a given percentage (p) of the species in the ecosystem
are not protected. For example, the HC5 is presumed to be protective of 95 percent of the
species in the ecosystem. The HCp is estimated with the mean and standard deviation of
experimentally determined NOEC data for several species representing a range of taxa. The
methodology can be used to calculate the HCp with two degrees of confidence, 95 and 50
percent By calculating the left 95 percent confidence limit of the HCp, the method
underestimates the critical concentration by 95 percent Calculating the median confidence
limit results in a best estimate of the critical concentration, i.e., the true value will be above
the HCp 50 percent of the time and below the HCp 50 percent of the time.
The equation used in the RTVM refined effects assessment procedure to calculate
confidence interval containing the HCp is as follows:
where xm is the sample mean of the NOEC data, sm is the sample standard deviation of the
NOEC data selected, m indicates the number of species-mean NOEC values, and the
parameter (kj) is the extrapolation constant for calculating the one-sided left-most confidence
limit for a 95 percent protection level on the basis of the logistic distribution. Aldenberg and
Slob (1993) prove that, for each value of m, ^hly one value of kj needs to be calculated, e.g.,
based on the standard logistic distribution.
In order to be consistent with the AWQC approach of protecting 95 percent of the
species in an ecosystem, p was set at 0.05 (i.e., p = 5). The value of kj was obtained from
Table 3 in Aldenberg and Slob (1993) for any sample size (m) and for two levels of
confidence, 50 and 95 percent, respectively. For example, using the cadmium data from van
Straalen and Denneman (1989), m - 7, x,,, = 2.236, and sra = 1.618. The value of kj, from
Aldenberg and Slob (1993), at 95 percent confidence is 1.78. With 50 percent confidence,
the estimate of the HC5 in mg contaminant per kg soil is:
HC5 = exd 2.236-1.78-1.618
0.53
(4-8)
The 50 percent value can be regarded as the best estimate of the HCp, whereas the
more conservative value can be interpreted as a "safe" value given the limitations in the data.
The 50 percent value will overprcdict as often as it underpredicts the HCp while the 95
percent value will overestimate HCp only 5 percent of the time, protecting 95 percent of the
soil fauna with a large degree of confidence.
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In order to reflect the level of confidence in the soil community benchmarks, each
benchmark was assigned to one of the following three categories:
r
• Adequate—All of the benchmarks assigned to this category fulfilled the eight
taxonomic data requirements described in Chapter 3. For each species, a NQEC or
LOEC/10 was identified with sufficient information on soil characteristics to calculate
a normalized effects levels. Appropriate studies were limited to exposure routes that
matched the spatial location of the soil organism.
• Provisional—The study data for this category were of equal quality as the adequate
category. However, the minimum data set was reduced to five of the eight
representative soil species.
• Interim—For benchmarks assigned to this category, NOEC and/or LOEC/10 data
existed for four representative soil species (Slooff, 1992; Okkerman et al., 1993).
More flexibility was assigned to the provisional category and studies were included
for a wider range of exposure routes (e.g., dermal application).
4.3.5 Fish and Aquatic Invertebrates
Adverse effects levels for fish and invertebrates were identified for endpoints ranging
from mortality to growth and reproductive effects. As with the birds and mammals, data
collection efforts were primarily concerned with adverse effects relevant to the maintenance
or sustainability of populations (e.g., fecund^-, growth, mortality). The effects levels for fish
and aquatic invertebrates were generally reported in units of milligram or microgram per liter.
However, unlike data on birds and mammals, community-based benchmarks—the
AWQC—were available for a number of constituents of concern.
The effects data were evaluated to select the most appropriate level to use as the
lexicological benchmark for fish and daphnids. It is important to note that, unlike effects
levels for mammals and birds, the effects levels for aquatic organisms are available as a .
medium-specific concentration (Le., microgram/liter). As a result, the most appropriate
adverse effects level was selected as the lexicological benchmark without regard to
differences in body weight between aquatic species. The minimum data set established for
fish and daphnids was essentially based on the Tier n guidelines proposed in the Water
Quality Guidance for the Great Lakes System and Correction; Proposed Rules (58 FR
20802). The Tier n guidelines establish a procedure to calculate a secondary chronic value
(SCV) when data are insufficient to estimate a final chronic value (FCV), as described in the
Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of
Aquatic Organisms and Their Use (Stephan et al., 1985). The Guidelines require acute
toxicity data representing eight taxonomic families (e.g., a fish from the family salmonids)
and chronic toxicity data for at least three of the eight families, including an acutely sensitive
freshwater species. In contrast, the Tier n methods require data on only one of the eight
genera and are based on a statistical analysis of AWQC data conducted by Host et al. (1991).
The authors developed adjustment factors (AFs) to account for the uncertainty in deriving
August 1995 4-35,
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chronic values using less than eight data points (see Figure 4-1) and a default acute to chronic
ratio (ACR) to ensure that the SCVs would be below the FCVs within a specified confidence
limit The difference between calculating an FCV and an SCV may be summarized as
follows.
The FCV is calculated in one of two ways.* If acceptable chronic toxicity data are
available on at least one species representing the eight different requirements in Figure 4-1,
the FCV is essentially the concentration corresponding to a cumulative probability of 0.05 for
appropriate species. If the chronic toxicity data do not meet the eight genera requirements,
the FCV is calculated by: (1) calculating a final acute value (FAV) in the same manner
described for chronic toxicity data, (2) estimating an ACR as the ratio of at least three
comparable (e.g., same species) acute and chronic toxicity studies, (3) dividing the FAV by
two, and (4) dividing the FAV/2 by the ACR. It is important to note that this description is a
simplification of the actual methods and does not address many of the nuances of study
selection and data interpretation. For example, if multiple chronic studies are available on the
same species, the geometric mean (i.e., Species Mean Chronic Value, or SMCV) is calculated
because the distribution "of sensitivities of individual species within a genus are more likely to
be lognormal than normal (Stephan et al., 1985).
The family Salmonidae in the class Osteichthyes;
One other family (preferably a comme-";ally, or recreationally important warmwater
species) in the class Osteichthyes (e.g. bluegill, channel catfish, etc.).
A third family in the phylum Chordata (e.g..fish, amphibian, etc.).
A planktonic crustacean (e.g., a cladoceran, copepod, etc.).
A benthic crustacean (e.g., ostracod, isopod, amphipod, crayfish, etc.).
An insect (e.g., mayfly, dragonfly, damselfly, stonefly, caddisfly, mosquito, midge, etc.).
A family in a phylum other than Arthropoda or Chordata (e.g., Rotifera, Annelida,
Mollusca, etc.).
A family in any order of insecJfir any phylum not already represented.
Note: Does not include study selection guidelines.
Figure 4-1. Data requirements for FCV calculation.
*For the sediment community, only the FCV (and not the FRY or FPV) was used to calculate the sediment
quality criteria.
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The SCV is calculated in essentially the same way. However, since the minimum data
set only requires-data from one to seven genera, the SCV is always calculated from an
secondary acute value (SAV). The SAV is calculated in the same way as the FAY and
divided by. the adjustment factor appropriate to the data set as presented in Table 4-6. This
value is then divided by an ACR or the default ACR of 18 to estimate the SCV. The Tier n
methodology was designed to generate SCVs that are below the FCVs (for a complete data
set) with a 95 percent confidence limit For this analysis, the minimum data set required at
least one data point for daphnids.
The hierarchy for deriving benchmarks (i.e., FCVs and SCVs) for fish and daphnids
may be summarized as:
i. FCV = FCV determined for sediment quality criteria (SQQ
ii. FCV = Tier I FCV derived from GLI data
iii. FCV = FCV from Ambient Water Quality Criteria (AWQQ
iv. FCV = draft FCV based on reports from EPA ERL-D and ERL-N
v. FCV = Tier I FCV estimated from AQUIRE data and open literature
vi. SCV = Tier H SCV estimated from AQUIRE data and open literature (with
daphnid). .
For data preferences v and vi, only studies regarded in AQUIRE as high quality and
that met the general conditions (i.e., exposure duration, mortality restrictions for life-cycle
tests) required by the Guidelines for Deriving Numerical National Water Quality Criteria for
the Protection of Aquatic Organisms and Their Use (Stephan et al., 1985) were included.
However, study data in v and vi were not sciiitinized at the level of an FCV calculated for the
Ambient Water Quality Criteria. For constituents such as metals and pentachlorophenol that
are significantly affected by water characteristics (e.g., pH, water hardness), the benchmarks
were adjusted to account for the input parameters developed for this analysis. For example,
the Agency has developed equations to adjust the concentration of metals according to water
hardness (e.g., the copper concentration is corrected for hardness using the equation
.0.8545 [in (hardness)]-!.^
c /•
As with the benchmarks for mammals and birds, it was apparent that the aquatic
toxicity benchmarks represent different levels of data availability and appropriateness.
Table 4-6. Adjustment Factors (Daphnid Data .Required)
Sample Size (number of FCV data requirements fulfilled)
1.2 34 5 6 7
21.9 13.0 8.0 7.0 6.1 5.2 4.3
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Specifically, the SCVs estimated using Tier n methodology were intended by the authors
(Host et al., 1990)-as narrative criteria. Indeed, a great deal of effort has been concentrated
on methods to estimate chronic toxicity values from acute toxicity data (Mayer et al., 1992,
1994; Sloof et al., 1986; Stephan and Erickson, n.d.; Stephan et al. 1985; Suter et al., 1983;
U.S. EPA 1993J). These methods vary in levels of complexity, attention to biological and
statistical relevance, regulatory acceptance, and conservatism. For example, Mayer et al.,
(1994) developed a comprehensive approach to predicting chronic lethality from acute toxicity
data in which simultaneous consideration is given to concentration, degree of response, and
time course of effect The predicted NOECs were within a factor of 2 of the M ATC limits
for lethality and did not vary by more than a factor of 3-6 in 97 percent of the trials on a
database of 18 chemicals and seven fish species. This approach constitutes groundbreaking
work in that it is one of the first approaches to simultaneouly consider exposure, degree of
response, and the time course of effect However, the predicted NOECs were frequently
higher than the corresponding MATC values, which, based on the work by Suter et al. (1987),
correspond to significant levels of effect (e.g., reductions in fecundity, hatching, and weight).
To address these concerns, the benchmarks selected for fish and aquatic invertebrates
(represented by daphnids) were assigned to one of three categories: adequate, provisional, and
interim. As with mammals and birds, these categories generally reflect the adequacy of the .
database and the appropriateness of the benchmark. The categories were defined as follows:
• Adequate—The benchmark selected was a final chronic value (FCV), in order of
preference, from the following sources: (1) an FCV determined for the sediment
quality criteria, (2) an FCV derived for the Great Lakes Initiative, or (3) an FCV
from an Ambient Water Quality Criteria document
• Provisional—The benchmark selected was a draft FCV, in order of preference, from
the following sources: (1) an FCV calculated by the USEPA Environmental Research
Laboratory in Duluth or Narragansett or (2) an FCV estimated from data extracted
from AQUIRE (or literature) meeting the general 1985 guidelines for study selection.
• Interim—The benchmark selected was a secondary chronic value (-SCV) estimated
using Tier n methods on data extracted from AQUIRE (or literature) meeting the
general 1985 guidelines for study selection. The data set contained at least one
usable data point on a daphnid species.
For data sets in which the benchmark^was an order of magnitude or more above (1) the
NOEC or LOEC/10 for reproductive, developmental, or growth and survival effects or (2) a
NOEC or LOEC/10 for adverse effects to aquatic plants, an "*" w.as placed next to the
category designation to indicate that adverse effects may occur at the benchmark.
4.3.6 Sediment Community
Toxicological benchmarks for nonionic hydrophobic organic chemicals were derived
using the methods presented in the Technical Basis for Deriving Sediment Quality Criteria for
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Nonionic Organic Contaminants for the Protection ofBenthic Organisms by Using
Equilibrium Partitioning (U.S. EPA, 19931). In essence, the methods are based on the
assumption that the partitioning of the chemical between sediment organic carbon and pore
water is at equilibrium. Consequently, the fugacity or activity of a chemical is the same for
each of the phases (e.g., pore water, sediment) and the organism receives an equivalent
exposure from a single phase in equilibrium or the equilibrated system (Di Toro et al., 1991).
In short, equilibrium partitioning means that the pathway of exposure is not significant
The sediment quality criteria (SQC) are proposed chemical concentrations for sediment,
normalized for organic carbon, that will protect 95 percent of the species, on average, in the
benthic community. The SQC are calculated according to the following equation:
SQC = Kp FCV (also SCV) (4-9)
where K_ is the sediment / pore water partition coefficient (L/kg) and FCV and SCV are the
final and secondary chronic values for aquatic organisms, respectively, as described above.
As this equation suggests, the SQC are calculated assuming: (1) that infaunal and epibenthic
species have a similar toxicological sensitivity to water column species on which the FCV
were based and (2) that the exposure to benthic species may be estimated from equilibrium
partitioning theory (i.e., Kp). These two assumptions are discussed briefly below; however,
the reader should refer to the technical document (U.S. EPA, 19931) for a detailed description
of the scientific rationale.
The assumption of approximately equi Jent sensitivity is a key component of the SQC
because it allowed for the use of an existing database on FCVs developed for the Ambient
Water Quality Criteria. This assumption was based on comparative toxicological data, which
strongly suggested that benthic species are not uniquely sensitive to contaminants (Di Toro et
al., 1991). The data also suggested that the most sensitive infaunal species (i.e., living in the
sediment) were typically less sensitive than the most sensitive epibenthic and water column
species (i.e., living at the sediment/water interface). Despite the paucity of acute- toxicity data
on benthic species, the sensitivities of water column species versus epibenthic and infaunal
species appear very similar, on average.
The second key assumption in developing the SQC is that the chemical concentration in
pore water is in equilibrium with the chemical concentration sorbed to organic carbon in the
sediment For sediments with foe > 0.2 percent by weight the organic carbon appears to be
the predominant phase for chemical stSption and these concentrations are related by a
partition coefficient K^, as shown in Figure 4-2 (Di Toro et al., 1991). The sediment/water
partition coefficient (Kp) presented in Equation 4-9 has been shown to be approximately equal
to the product of f^ and K^, where f^ is the mass fraction of organic carbon in sediment and
K^ is the chemical-specific partition coefficient (i.e., Kp - f^ K^.). Di Toro et al. (1991)
also demonstrated that for sediment pore water partitioning, the organic carbon/water
partition coefficient (K^ is essentially equal to the octanol/water partition coefficient
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Sediment - Pore Water Exposure
Sediment
Carbon
Pore
Water
Figure 4-2. Dlustration of exposure pathways in equilibrium partitioning.
Thus, Equation 4-9 can be rewritten as
SQC »/„ K- FCV (also SCV)
(4-10)
or equivalently as
SQC - fx K^ FCV (also SCV).
(4-11)
Because the SQC are linearly related to the mass fraction of organic carbon (f^, this
relationship can be expressed as:
FCV (alsoSCV)
(4-12)
' oc
and SQC^ is defined as the organic carbon-normalized SQC concentration given by
-*»
SQCoc
SQC
fo
(4-13)
i oc
Equation 4-12 becomes
oc
FCV (alsoSCV) .
(4-14)
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The implication of Equation 4-14 is that, for a specific chemical having a specific K^, the
organic-carbon normalized sediment concentration (SQC^ is independent of sediment
properties (Di Toro et al., 1991). However, for this analysis, the sediment f^ was assumed to
be 0.05, the mean value of the range suggested in the Addendum: Methodology for Assessing
Health Risks Associated with Indirect Exposure to Combustor Emisssions (U.S. EPA, 1993a).
It should be noted that lexicological benchmarks for the sediment community were not
derived for metals or other constituents that did not fall into the category of nonpolar,
nonionizable organic chemicals. The EPA is currently evaluating methods to establish SQC
for metals using the acid volatile sulfide approach (see Di Toro et al., 1990, 1992; Casas and
Crecelius; 1994). However, these methods have not been adequately reviewed for use in a
regulatory context
The development of benchmarks for the sediment community relied on the methods
presented above for selecting and estimating FCVs and SCVs for fish and daphnids. For
completeness, the categories are repeated below:
• Adequate—The sediment benchmark was based on a final chronic value (FCV), in
order of preference, from the following sources: (1) an FCV determined for the
sediment quality criteria, (2) an FCV derived for the Great Lakes Initiative, or (3) an
FCV from an Ambient Water Quality Criteria document
• Provisional—The sediment benchmark was based on a draft FCV, in order of
preference, from the following sources: (1) an FCV calculated by EPA's
Environmental Research Laboratory in Duluth or Narragansett or (2) an FCV
estimated from data extracted from AQUIRE (or literature) meeting the general 1985
guidelines for study selection.
• Interim—The sediment benchmark was based on a secondary chronic value (SCV)
estimated using Tier n methods on data extracted from AQUIRE (or literature)
meeting the general 1985 guidelines for study selection. The data set contained at
least one usable data point on a daphnid species.
For data sets in which the benchmark was an order of magnitude or more above (1) the
NOEC or LOEC/1Q for reproductive, developmental, or growth and survival effects or (2) a
NOEC or LOEC/10 for adverse effects to aquatic plants, an "*" was placed next to the
category designation to indicate that adverse effects may occur at the benchmark.
•*&
4.3.7 Aquatic Plants
Adverse effects levels were identified for vascular aquatic plants as well as for aquatic
microphytes (i.e., algae) because algae (1) have a relatively long history of toxicity testing
and (2) have been shown to be more sensitive receptors than vascular aquatic plants
approximately 80 percent of the time (Klaine and Lewis, 1995). Data collection efforts were
focused on endpoints thought to be highly relevant in terms of the ability of plants to support
higher trophic levels. For example, growth inhibition, decreased cell numbers, and reduction
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in carbon fixation are.endpoints frequently associated with algal toxicity tests. Similarly,
ehdpoints for vascular plants such as duckweed (e.g., Lemna minor) included development of
fronds, biomass, root number and length, and plant number. The database on aquatic plant
toxicity was found to be reasonably complete for algal tests, especially for metals and
pesticides. However, the majority of data on algal toxicity was based on the results for a few
green algal species such as Selenastrwn capricornutwn, Chlorella vulgaris, and several
Scenedesmus species. Toxicity data on vascular aquatic plants were relatively sparse and, as
with terrestrial plants, the utility of the data set was limited. Because few data were available
on aquatic macrophytes and microphytes, the minimum data set consisted of one study on
chronic or subchronic effects on algae or vascular aquatic plants.
The primary data sources for toxicity data on aquatic plants were the Toxicological
Benchmarks for Screening Potential Contaminants of Concern for Effects on Aquatic Biota:
1994 Revision (Suter and Mabrey, 1994) and the Aquatic Information Retrieval (AQUIRE)
database developed by the U.S. EPA Environmental Research Laboratory in Duluth. Toxicity
data in both of these sources were identified on algae and vascular aquatic plants. However,
since only two data sources were used, relatively simple criteria were established for study
selection:
• Studies on biologically meaningful responses in algae (e.g., growth inhibition, cell
number) and vascular plants (e.g., biomass, development of fronds) were preferred.
Because the life cycles of microalgae in a rapidly growing culture are much shorter
than both test durations and most effluent releases, the laboratory test results may be
considered to be population-level responses (Suter, 1993a).
• For vascular aquatic plants, the lowest observed effect concentrations (LOECs) were
preferred to ho observed effects concentrations (NOECs), providing that the LOEC
study contained concentration response data.
• For algae, the lowest effects concentration such as an EC^o or EC50 was selected as
the benchmark. Given the variability in sensitivity among different species of algae,
the relatively short life cycles of algal test species, and the unknown impact of
biological adaptation to chemical stressors on algal communities, setting benchmarks
for algae based on no effects concentrations was considered to be overly
conservative.
• The aquatic plant benchmarks-presented by Suter and Mabrey (1994) were used when
available since this report had previously screened toxicity data on aquatic plants and
identified the lowest effects concentration. Algal tests from the Suter and Mabrey
report were at least 96 hours in duration and considered any biologically meaningful
response.
• If a benchmark was not available in Suter and Mabrey (1994), the lowest adverse
effects concentration on vascular aquatic plants or algae (> 96 hour duration)
identified in AQUIRE were used, provided the studies were classified with a review
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code "1" (thorough methods and results documentation) or "2" (documentation
generally-satisfactory).
Considering that-aquatic plants provide the habitat for other aquatic organisms, are
important participants in nutrient cycling, oxygen production, and carbon assimilation, and
serve as a food source at the base of the aquatic food chain, it is crucial to include aquatic
plants among the suite of ecological receptors. Consequently, it is crucial to consider algae in
addition to vascular plants as important ecological receptors in the generic aquatic ecosystem.
However, the paucity of data on vascular aquatic plants and the difficulty of delineating the
ecological significance of algal test endpoints suggest that potential benchmarks should be
interpreted with caution. Based on the importance of aquatic plants to the freshwater
ecosystem and the apparent deficiencies in the database on aquatic plant toxicity, the
benchmarks for aquatic plants were selected as the lowest effects concentration for algae (e.g.,
EC20. EC50) or the lowest LOEC for vascular aquatic plants.
To reflect the uncertainty in assigning ecological significance to aquatic plant responses
as well as the limited database on aquatic plant toxicity, the benchmarks were designated
according to the following categories:
• Adequate—No benchmarks were assigned to this category. Test endpoints for
effects on algae have been less well standardized and their relevance to the field are
less clear than for animals (Lewis, 1990). Relatively few tests of effects on vascular
aquatic plants have been conducted and development of culture techniques, test
methods, and sensitive endpoints has been limited (Klaine and Lewis, 1995). Further
research is needed to develop more realistic test designs to evaluate the effects on
natural algal assemblages and vascular aquatic plant communities. In addition,
methods have not been developed to predict the ecosystem impacts from laboratory-
derived toxicity results on aquatic plants (i.e., the ecological significance).
• Provisional—No benchmarks were assigned to this category. A benchmark would
have been designated as provisional if the following conditions were met: (1) the
benchmark study provided a LOEC for a vascular aquatic plant estimated from at
least two data points or the lowest EC^o value from representative algal species, (2)
phytotoxicity studies were available on at least one-species of floating macrophytes,
one species of submersed aquatic vegetation, and one species of emergent aquatic
vegetation, and (3) EC^o values were available for at least three of the six algal
classes proposed by Swansoc^t al. (1991), including green and blue-green algae,
diatoms, and dinoflagellates.
• Interim—All of the benchmarks were assigned to this category. The benchmark
selected was the lowest LOEC identified for vascular aquatic plants or the lowest
effective concentration (EC^) identified for a species of freshwater algae. As stated
above, laboratory studies on algae were considered to produce population-level
benchmarks and, for vascular plants, only endpoints from which effects could be
inferred at the population level (e.g., growth) were considered appropriate.
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4.3.8 Uncertainties and Issues of Concern
The selection of the suite of receptors, or assessment endpoints, described in Section
3.3 was intended to confer a protection to the entire ecosystem by representing key structural
and functional components (e.g., producers, grazer guilds, predators). However, in
establishing measurement endpoints for these assessment endpoints, a number of uncertainties
and issues of concern were identified concerning the development of lexicological
benchmarks. In particular, the knowledge uncertainty in the database used to establish
benchmarks for ecological receptors was considered the most significant source of
uncertainty. Depending on the receptor being assessed, data at the organism level were barely
adequate for some chemicals and virtually absent for others. In addition, the modeling
uncertainty introduced by inferring population or community-level effects from data on
individual effects also represents a significant, if somewhat lower, source of uncertainty.
Although it is reasonable to infer population effects from data on individual fecundity, the
response of populations in the wild are likely to be far more complex than the inference
assumes. Specific areas of uncertainty and issues of concern are discussed below.
4.3.8.1 Conceptual Approach
4.3.8.1.1 Protection to Ecosystem Inferred from Other Levels of Organization
>
The uncertainty in assuming that protection of populations of representative species and
communities will protect the behavior and properties at the ecosystem level is considerable.
Indirect effects that may be important in an ecosystem, such as predator-prey relationships,
food webs, competition and other intra-ecosystem interactions, are not directly, measurable
from the endpoints used in this analysis. As stated in Section 3, the inference that the
ecosystem is protected from chemical stressors by protecting key components that can be
measured relies heavily on the assumption that the correct components can be assessed. For
example, low concentrations of chemical stressors, while not toxic, may elicit avoidance
behavior from key species within an ecosytem. The avoidance may cause significant changes
in the dynamics of predator-prey interactions, ultimately leading to significant changes in the
properties of the ecosytem (e.g.. species abundance). Unfortunately, tools for ecosystem-level
modeling are currently inadequate to determine protective concentrations for generic,
ecosystems. Despite the uncertainty in trying to confer protection to an ecosystem by
protecting some of its parts, the methodology developed in this analysis was considered to be
the most reasonable approach available.
'*s> . •
4.3.8.1.2 Biological vs. Statistical Relevance .
The use of no effects concentrations are questionable from a statistical standpoint
(Smith and Cairns, 1993). The NOECs and NOAELs are generated using .hypothesis-testing
statistics and, therefore, the quantification of no effects levels depends critically on the size
arid variability of an experiment; smaller and less precise experiments lead to higher values
for the no effects level (Hoekstra and Ewijk, 1993). LOECs and NOECs (and resultant
MATCs) derived using hypothesis testing have resulted in some chronic benchmarks for
n.-,nnim-.'.un.umjuuuuu^^ - niinirinirniiniiiiiiinninniMiiiiMiiniiiii""^^
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aquatic organisms being set at concentrations causing greater than 50 percent mortality
(Stephan et al., 1-9S5). The selection of measurement endpoints is inherently biased toward
statistical relevance and not biological relevance. In practice, NOECs and NOELs are used as
no-effects levels even though the level of biological response may be significant For
example, the MATC (calculated as the geometric mean of the reported LOEC and NOEC) has
been shown to correspond to fairly high levels of effect Data from 176 tests on 93
chemicals with 18 species indicated that average reductions in reproductive endpoints at the
MATC were 20 percent for parental survival, 42 percent for fecundity, and 35 percent for an
integrative weight/egg parameter (Suter et al., 1987). Although the data requirements for
alternative approaches such as the benchmark-dose approach are considerable, additional
research may be needed to provide benchmarks that better represent biological significance.
4.3.8.13 No Effects vs. Lowest Effects
The benchmarks for ecological receptors are generally based on no effects
concentrations (or levels) instead of lowest effects concentrations (or levels). This level of
conservatism was considered appropriate since the exit criteria are, in effect, "walk away"
concentrations for the RCRA hazardous waste management system (i.e., Subtitle C).
However, it has been suggested that a 20 percent reduction in a population is an appropriate
measurement endpoint since it is the approximate limit of detection for field measurement
techniques on populations (Suter et al., 1992b). It is unclear that the detection limit for
population effects is also biological significant; however, natural population variability may
exceed 20 percent for a wide variety of species.* Moreover, the presumption that
benchmarks must be protective of successive nopulations of organisms as well as the
immediate threat to reproducing individuals does not allow for recovery mechanisms or
adaptation and may add to the conservatism. Although models are available to assess
population impacts from individual measurement endpoints (e.g., DeAngelis et al., 1990),
these models are limited in their application and difficult to validate.
4.3.8.1.4 Reliance on "Population-Type" Effects
In recognizing that population data are not available, the Science Advisory Board
pointed out that population impacts must be inferred from effect to individuals (U.S. EPA,
19931). Consistent with SAB recommendations, endpoints were selected for which population
impacts could be reasonably demonstrated (e.g., fecundity, lethality). However, in adopting
these "population-type" endpoints as lexicological benchmarks, it was important to consider
the entire data set, including endpoins&ihat may pose significant risks to individual organisms
in the absence of clear impacts at the population level. In some cases, "nonpopulation"
effects levels (e.g., liver pathology, immunological deficit) were not used to establish
benchmarks because impacts at the population level could not be reasonably inferred. For
constituents with steep dose-response curves (i.e., high chemical activity), exceeding a
•Population variability is dependent on selecting an appropriate temporal scale for the species in question.
This statement was intended as an illustrative example of issues regarding population variability.
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"nonpopulation" effects level may result in toxic insult to a large percentage of the
population. For-example, because the lifetime reproductive value for a young organism may
be substantially less than the value of an organism that has reached reproductive maturity
(Soul6, 1987), adverse-effects to a sufficient number of adult organisms may indirectly
diminish the reproductive fitness of the population (e.g., through the inability to avoid
predation). Ignoring target organ studies or effects levels that may not be directly relevant to
population impacts, particularly when those effects levels are below reproductive effects
levels, may result in criteria that are underprotective of reproductively mature organisms.
4.3.8.1.5 Category Approach
Generally, the adequate category was applied to a benchmark derived from a no effect
level and the data set provided information on a suite of endpoints (e.g., life-cycle,
developmental, growth). The provisional category was applied to a benchmark that was
extrapolated from a lowest effects level to a no effects level. The interim category included
benchmarks that were derived with minimum data and associated with high uncertainty.
Although the category approach provides a useful metric of uncertainty—a kind of comfort
zone—the categories have not been validated quantitatively. Nevertheless, the categories
acknowledge the uncertainties in the different methods and data sets used to estimate
lexicological benchmarks. (Given the substantial gaps in lexicological data, providing a
qualitative uncertainty category may be the most appropriate way to address uncertainty
associated with ecological 'benchmark development
4.3.8.1.6 Laboratory to Field Extrapolation
The lexicological benchmarks for ecological receptors were developed assuming that
effects that are observed in laboratory test species are applicable to wildlife species under
similar field conditions. As a result, there were no laboratory-to-field extrapolation factors
applied to account for the additional stress that may be encountered under field conditions
(e.g., cold or drought). Van Straalen and Denneman (1989) and Stephan et al. (1985)
examined arguments both for and against a laboratory-to-field extrapolation factor and
concluded that laboratory-to-field extrapolation factors were not necessary; i.e., criteria
derived with laboratory data should protect soil fauna in the field. However, other authors
have suggested that laboratory species tend to be more homogeneous and have narrower
tolerance distributions than their field counterparts, and that the distribution of. the target
population of species is likely to have a different shape and scale relative to the laboratory
species (Smith and Cairns, 1993; Sut$£et al., 1983; Seegert et al., 1985). As a result, the
distribution of the endpoint will be narrower for the laboratory species. In addition, Smith
and Cairns (1993) point out that local adaptation to conditions may make an individual
species more or less tolerant to .a chemical stressor. The authors conclude by stating that the
extent and variation between laboratory and field species have not been investigated, although
information on clonal variation to chemical stress in laboratory species is available from Baird
et al. (1990). .
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4.3.8.2 Mammals and Birds
4.3.8.2.1 Inference of Population Effects .
The ability of the population to sustain itself (within normal biological variation) was
inferred from individual effects such as fecundity because population modeling was
considered to be beyond the state-of-the-scierice in this regulatory application of ecological
risk assessment However, this inference has yet to be validated from field or microcosm
studies on exposed populations. Without validation, it is likely that some benchmarks are
overprotective and others are underprotective of wildlife populations. Given natural
population variation and the ability of wildife to adapt to stressors, the benchmarks may be
overprotective in field conditions. .
4.3.8.2.2 LOAEL-to-NOAEL Safety Factor
The safety factor of 10 used in LOAEL-to-NOAEL extrapolation was adopted from the
RfD methodology (Barnes and Dourson, 1988). However, the RfD-derived safety factor is
conservative and is considered to be at the upper end of the range for possible LOAEL-to-
NOAEL extrapolation factors, with average safety factors of approximately 5. Since the
LOAEL-to-NOAEL extrapolation accounts for the dosage spacing in the study, further
research is needed to develop extrapolation factors for the ecological risk assessment
paradigm that utilizes: (i) dose-response information (i.e., the benchmark-dose approach) or
(2) data inference on constituents with similar chemical activities (i.e., quantitative structure
activity analysis).
4.3.8.2.3 Interspecies Uncertainty
For mammals and birds, differences in interspecies uncertainty were indirectly
addressed through the use of the species-scaling equation described in Section 4.3.2.
However, Opresko et al. (1994) point out that the method has not been applied to wildlife by
EPA and that wildlife lexicologists commonly scale dose to body weight without
incorporating the exponential factor. In addition, scaling may not account for physiological/
biochemical differences in species sensitivity. Differences in sensitivity that are contrary to
estimates produced by the scaling equation have been demonstrated for several chemicals
(e.g., dioxin). Based oh this information, the scaling equation was used only if the chemical
database did not suggest sensitivity differences that were not consistent with the scaling
equation. However, the case-by-case approach relies on heterogeneous data sets that seldom
contain sufficient data to assert differences in sensitivity. An alternative approach might
consider using a graded scale of safety factors from 1 to 10 in increments of 2 following the
safety factor of 2 (i.e., 1, 2, 4, 6, 8, 10). Conditions could be assigned to each safety factor
reflecting: (1) the overall quality of the data set, (2) the magnitude of the effects level
compared to other effects levels in the data set, (3) the taxonomic distance between the study
value and the ecological receptor species, and (4) differences in sensitivity suggested by the
data set This approach may be appealing because it allows for different safety factors to be
applied to different data sets and consolidates a weight-of-evidence approach into one safety
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factor. However, the increments across safety factors are somewhat arbitrary; a safety factor
of 6 does not necessarily mean that the uncertainty is three times greater than a safety factor
of 2. In the absence of empirical data supporting a toxicodynamic approach to interspecies
sensitivity, differences in sensitivity were addressed on a case-by-case basis, using the scaling
equation as appropriate to the data set
4.3.8.2.4 Timing of Exposure
\
For some chemicals, the timing of exposure appears to be a more important determinant
of toxicity than the dose. Developing young may be very sensitive to chemical stressors
because they are: (1) more highly exposed per kilogram of body weight, (2) often have
higher metabolism (e.g., rapid cell division) than mature adults, (3) have incompletely
developed immune systems, and (4) may have acquired a previous body burden from the
parent Moreover, the impacts on a developing organism may be more severe in the wild
than in the laboratory since wildlife are exposed to other stressors (e.g., thermal, predation) in
addition to chemical stressors. Unfortunately, few data are available with which to
characterize the normal growth and development of young organisms. For example,
physiological estimates of dietary intake for juveniles are available for only a few species of
birds and mammals. In addition, many species are particularly sensitive to chemical stressors.
during the early stages of gestation. Studies have demonstrated a severe increase in fetal
mortality in pregnant dams exposed to 2,4,5-trichlorophenoxyacetic acid that experienced a
short period of water deprivation (Smith et al., 1981). Considering the number of additional
stressors. likely to influence gestating wildlife, a greater level of conservatism may be required
for the lexicological benchmarks.
4.3.8.2.5 Cross-Species Scaling for Birds
The scaling equation used for mammals was also applied to avian benchmarks because
the empirical data suggest that the scaling equations are equally vital for birds. As a result
effects levels for laboratory chickens or pheasants were adjusted for differences.in size in
very different ecological receptors (i.e., predatory birds).
4.3.8.2.6 Inhalation Route of Exposure
Air inhalation as an exposure route was not considered for birds and mammals for
several reasons. First measurement endpoints for inhalation exposures seldom include
reproductive effects or other effects -likely to be associated with population impacts. Second,
wildlife may spend varying amounts of time within different locations in the habitat (e.g.,
ground level and tree canopy). As a result .air concentrations would need to be modeled so
that both the spatial and temporal characteristics of an animal's lifestyle are represented.
Fourth, the air models used in this analysis (see Section 6) are not designed to model air
concentrations relevant to animals that live in forest interiors. However, the inhalation of
subsurface ak may be an important route of exposure for ground-dwelling animals such as the
ground squirrel. At least one study has demonstrated that the risk from inhalation of
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subsurface air (for volatile chemicals) is several orders of magnitude higher than the risk from
soil ingestion (Garrsen, 1993).
4.3.8.2.6 Dermal Route of Exposure Excluded
The dermal route of exposure was not included for birds and mammals. Although
some exposure scenarios may be associated with dermal exposures (e.g., ground-dwelling
animals), toxicological data to evaluate this route of exposure are insufficient For example,
little information is available on the absorption coefficient for chemicals across fur or
feathers.
4.3.8.3 Terrestrial Plants
4.3.8.3.1 Biological Significance of Effect
The measurement endpoints for plants were limited to growth and yield parameters as
explained above (i.e., ecologically significant). This is consistent with the Registration Data
Requirements under the Federal Insecticide, Fungicide, and Rodenticide Act (FEFRA), such as
seed germination, seedling emergence, and vegetative vigor (56 FR 54852-54862). However,
the LOEC values in the Oak Ridge database (Will and Suter, 1994) are the highest applied
concentration of the chemical stressor, which elicited <20 percent reduction in a measured
response. It is not clear whether this level of response or other responses reported in
PHYTOTOX are biologically significant to populations of wild plants. In addition, other
effects such as RNA synthesis or C02 uptake may be more sensitive indicators of potentially
significant risks to plants. Clearly, further research is needed to define: (1) the most
sensitive, biologically significant endpoint for plants (e.g., seed germination, early growth),
and (2) the effects level at which the effect should be considered significant in terms of plant
population growth and survival.
4.3.8.3.2 Data Gaps
Data on phytotoxicity were sparse for most groups of chemicals, with the majority of
data on metals and pesticides for domestic cultivars such as com, lettuce, and potatoes.
Efforts to supplement the database on plant toxicity have proven to be time- and resource-
intensive because plant studies are found in a much wider range of information sources than
toxicological data on other receptors. Because of the paucity of data, neither representative
plant species nor standard test types,(g.g., chronic tests) were determined, and the 10th
percentile LOEC or the lowest LOEC was used as the benchmark for plants. The lack of
standardized testing protocols for plants may need to be resolved through new research
intitiatives rather than additional literature reviews. Research needs notwithstanding, the lack
of phytotoxicity data greatly increases the uncertainty in developing benchmarks for plants.
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4.3.8.3.3 Extrapolation from a Single Plant Species
The phytotdxicity benchmark for some chemicals was developed from toxicity data oh a
single species of plant Therefore, if the lowest effects concentration was identified for
lettuce, that benchmark was used to represent all the plants in an ecosystem, including forage
grasses, flowers, trees, and countless other species of plants. Given the differences in plant
physiology among these genera (e.g., root systems) and metabolic pathways (e.g., ring-methyl
oxidation), using the study value from one plant species, without adjusting for differences in
physiology and metabolism, introduces additional uncertainty.
4.3.8.3.4 Differences in Soil Characteristics
Soil characteristics such as pH, clay type and content, iron oxide content, and organic
carbon affect the ability of chemicals to be taken up by plants. As a result, phytoxicity is
partially determined by soil characteristics. For example, cadmium bioavailability is strongly
influenced by the iron and, therefore, cadmium phytotoxicity is expected to be lower in soils
rich in iron oxide (Alloway and Jackson, 1991). The bioavailability of many organic
constituents is dependent on the fraction of organic carbon (f^ present in the soil and the
organic carbon partition coefficient of the specific chemical (Bromilow and Chamberlain,
1995). Therefore, in soils with high levels of organic carbon (Le., high f^, bioavailability
would be reduced and a higher concentration would be required to cause toxic effects than for
soils with low organic carbon. Although f^ and pH are reported in some studies, the
empirical relationship between various soil characteristics and bioavailability is just beginning
to be delineated in plant uptake models (Trann, 1995). Differences in soil characteristics
create uncertainty in the phytotoxic benchmarks, possibly by an order of magnitude or more,
since f^ varies over an order of magnitude (Carsel et at, 1988).
4.3.8.4 Soil Community .
4.3.8.4.1 Soil Quality Characteristics
As is the case with plants, various physicochemical soil characteristics such as'pH,
organic matter, and clay content affect the toxicity of a constituent on soil organisms. As a
result, the toxicity of a constituent is partially determined by the characteristics of the soil in
which the exposure takes place. For example, the toxicity of copper to the earthworm
Octolasium cyanewn is highly correlated with the organic matter content of the soil (Jaggy
and Streit, 1982). Likewise, the toxis&y of chlorophenols was shown by van Gestel and Ma
(1990) to be correlated with both pH and organic matter. From data on bioaccumulation in
earthworms, it can be concluded that the bioavailability of metal may depend on pH, organic
matter content, cation exchange capacity, and clay content (van Gestel and van Straalen,
1994). However, the quantification of each factor to the toxicity and bioavailability of metals
is almost impossible.
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4.3.8.42 Distribution of the Toxicity Data
The RTVM methodology makes assumptions regarding the distributions underlying the
toxicity data. The first is the distribution within individual species responding to varying
concentrations of a toxicant This is the response of individual species as a measure of the
parametric response of all the members of that species. The second is the distribution across
species, i.e., within a taxonomic group. This is the response of different species as a measure
of the parametric response of all the members of a given genera, class, family, etc.
The response of an individual species to a toxicant as a measure of the response of all
of the individuals of that species is assumed to follow a lognormal distribution. In instances
where there is more than one reported toxicity value (i.e., multiple NOEC values for a given
endpoint and species), the geometric mean of the values is used to calculate a final NOEC
value. The geometric mean, rather than the .arithmetic mean, of the NOEC values is used
because the distributions of sensitivities of individual organisms in toxicity tests are more
likely to be log-logistic than normal (Kooijman, 1987; Slooff, 1992). This is also the case in
the derivation of AWQC (Stephan et al., 1985). •
In general, the type of distribution (i.e. normal, logistic, triangular) of the toxicity data
across species cannot be verified well with the small sample sizes typically used in toxicity
testing. Slooff (1992) requires a test (Empirical Distribution Function Test) as part of the
RIVM methodology to determine if the toxicity data fit a logistic distribution assumption, but
concedes that the power of the test is weak and that only major deviations from the logistic
distribution can be detected. However, as shown by Smith and Cairns (1993; Table 6.1) the
extrapolation constants (i.e., parameter d,,, in equation 4-7 in section 4.3.4) used for
calculating an HCp are not substantially different when-using a logistic or normal model.
Given all of this however, it is unlikely that the actual function used to describe the
distribution is critical, relative to the uncertainty introduced by other assumptions (Smith and
Cairns, 1993). Furthermore, Romijn et al. (1993) suggest using the data regardless of their
distribution so long as at least eight data points are available.
While the choice of distribution may not lead to large differences in a numerical.
standard (Smith and Cairns, 1993), the selection of a distribution has more than a just a
theoretical implication. In selecting the triangular distribution for the development of AWQC,
Stephan et al. (1985) implicitly assume that there is a threshold concentration below which no
effects will be observed. Slooff (1992), and van Straalen and Denneman (1989), on the other
hand, presuppose no threshold concentration in their selection of the logistic distribution.
4.3.8.4J Percentage of Species Protected
There is uncertainty concerning the relationship between the selected level of
protection, 95 percent, and degree of protection that this level affords the community. The
HCp, with p equal to 5, is thought to protect a soil community because it is presumed that, by
protecting 95 percent of all species (i.e., 5 percent of the soil fauna are presumed not to be
protected), communities will not be unacceptably harmed. Likewise, the AWQC also assumes
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that, if acceptable data are available for a large number of appropriate taxa from an
appropriate variety-of taxonomic and functional groups, a reasonable level of protection (i.e.,
95 percent protection) may be provided if all except a small fraction of the taxa are protected
(Stephan et al., 1985). The Dutch, however, are forthright in the development of their
methodology and state that the selection of a 95 percent protection level for ensuring
ecosystem function is a risk management decision with little scientific basis (DGM, 1990).
4.3.8.5 Aquatic Life
4.3.8.5.1 Species Sensitivity
The FCVs and SCVs represent a statistically significant rather than a biologically
significant threshold and may not afford sufficient protection for aquatic organisms. For
example, Kszos et al. (1992) found that nickel concentrations on the Oak Ridge Reservation
that are below the FCV are toxic to daphnids. A number of alternative approaches exist to
account for differences in sensitivity, particularly for fish. The EPA Office of Water
Regulations and Standards (OWRS) based their uncertainty factor for taxonomic variance on
the results of Kemerle et al. (1983), in which the authors looked at combinations of four
commonly tested aquatic animals—Daphnia, fathead minnow, rainbow trout, and bluegill—as
predictors of the most senstitive species (Suter, 1993a). The OWRS uses a factor of 10 for
species sensitivity if a vertebrate and daphnid have been tested Aldenberg and Slob (1993)
proposed calculating a one-sided 95 percent left confidence limit from the mean and standard
deviation of a sample of laboratory toxicity data, assuming a logistic distribution. Suter et al.
(1983) devised a system based on taxonomic relationships between tested species and the
species of interest in which regressions were performed between all pairs of species that occur
in a common genus, all pairs of genera within common families, families within orders, etc.
(Suter, 1993a).
4.3.8.5.2 Use of Secondary Chronic Values (SCVs)
The SCVs used as benchmarks for aquatic life were developed using the Tier n
methodology based on the work of Host et al. (1991). However, despite the extensive
statistical analyses of the data, the authors indicated that the SCVs should be used only for
narrative purposes. It seems likely that the SCVs will tend to be overprotective relative to the
FCVs.
4.3.8.5.3 Use of Proposed Method -Car Sediment Benchmarks
The benchmarks for the sediment community were based on the proposed methods for
Sediment Quality Criteria (U.S. EPA, 19931). Although the methods have been peer
reviewed, there may be changes in the proposed methods pending final review by the Agency.
The authors point out that the sediment criteria derived using the equilibrium partitioning
approach may vary from the observed sediment effects concentration by approximately a
factor of 2 to 3. In addition, the partitioning theory that forms the basis of the SQC equation
(see above) is currently under scrutiny by the Agency. These caveats notwithstanding, the
August 1995 4-52
-------
4.0 ENDPOINTS
proposed SQC methods represent the best science available for estimating sediment concentra-
tions.
4.3.8.5.4 Sediment Benchmarks Not Included for Metals
Benchmarks for metals in the sediment community were not proposed in this analysis
because: (1) the SQC methods are not applicable to metals and (2) the Agency is working on
SQC for metals based on the acid volatile sulfide (AVS) concentration in sediment Although
preliminary analyses on this work are not currently available, a number of scientists have
published studies on the relationship between AVS and toxicity in sediments (e.g., Casas and
Crecelius, 1994; Di Toro et al., 1990, 1992). Since sediments are known to be an important
sink for trace metals in the aquatic systems, this exclusion constitutes a serious data gap in
the ecological exit criteria for metals.
4.3.8.5.5 Benchmarks for Aquatic Plants
r
. Much of the discussion of uncertainty on terrestrial plant benchmarks applies to aquatic
plant benchmarks as well and need not be repeated here. Nevertheless, there is a fundamental
lack of data that establishes the link between test data on toxic endpoints in algae and
vascular aquatic plants and biological significance in the field. Particularly with respect to
algae, the long-term impacts on ecosystems at laboratory effects concentrations (e.g., ECjQ,
EC50) are not well understood. Consequently, it is difficult to assign ecological significance
to these endpoints and effects levels with certainty.
August 1995 4-53
-------
5.0 E ?POSlT--.e 5.1 Introduction
SECTION 5.0
EXPOSURE
5.1 INTRODUCTION
In this analysis, exclusion levels in waste, soil, or groundwater were backcalculatcd
from a target individual lifetime risk of 10~6 for carcinogens or a hazard quotient of 1 for
noncarcinogens. This section describes the exposure component of the analysis: how
concentrations in exposure media were backcalculated from risk or hazard quotient. Section 6
.describes the fate and transport component of the analysis: how emissions from the waste
management units (WMUs) were backcalculated from the exposure media concentrations.
Section 7 describes the WMU component of the analysis: how waste concentrations were
backcalculated from emission rates. •
This section develops the linkage between effects levels (i.e., human health benchmarks
or ecological effects endpoints) and concentrations in the exposure media (i.e., air, soil,
surface water, plants, animal products). For human health effects, standard equations were
used for the ingestion of contaminated media, and exposure assumptions were adjusted to the
exposure scenario.
This section is divided into two major subsections: Section 5.2 addresses human health
exposure scenarios and Section 5.3 addresses ecological exposure scenarios.
August 1995 5-1
-------
5.0 EXPOSURE S3 Concentrations for Human Receptors
5.2 CONCENTRATIONS FOR HUMAN RECEPTORS
This subsection describes the calculation of exposure media concentrations for human
exposure scenarios. Section 5.2.1 describes the conceptual approach; Sections 5.2.2 through
5.2.8 describe the specific calculations for air, soil, water, plants, beef and milk, fish, and
breast milk, respectively. Equations are presented in these sections with all inputs shown and
a reference to the appropriate subsection of Section 5.2.9, which describes the derivation of
all inputs used in Sections 5.2.2 through 5.2.8. For some parameters, inputs are not given;
instead, one of the following notations is used:
• Calculated—This is the parameter calculated by the equation shown.
• See Equation x-x—The parameter is calculated by the referenced equation. This
may be either the result of the previous step of the backcalculation or a calculated
input
• Chemical-specific—The parameter is chemical-specific. Values for chemical-specific
parameters are presented in Appendix A.
Inputs are shown in three columns, as follows:
• Default values—These are standard defaults used in Agency risk assessment or
policy used in this rulemaking.
• Central tendency values—These are approximately median (50th percentile) or
average values from available data.
• High-end values—These are approximately 90th percentile values from available
data.
Section 5.2.10 discusses uncertainty.
5.2.1 Conceptual Approach
The analysis begins with a target human health benchmark and exposure assumptions
tailored to each receptor and backcalculates target media concentrations. In characterizing the
exposure scenario, two exposure parameters are set to high-end values and the rest of the
exposure parameters are set to central tendency or regulatory default values. The two high-
end exposure values were typically exposure duration and a variable affecting intake of, or
exposure to, a contaminant (e.g., fraction contaminated, consumption rate, inhalation rate).
Receptor characteristics such as body.weight and skin surface area were set to default values.
The specific high-end exposure parameters for each receptor/media combination are identified
in the appropriate subsection.
August 1995 . 5-2
-------
5.0 EXPOSURE 52 Concentrations for Human Receptors
The algorithms used for backcalculating media concentrations are based on standard
risk equations used in most Agency risk assessments. For all inhalation and ingestion
pathways, these equations were adapted from Risk Assessment Guidance for Superfund
(RAGS): Volume I—Human Health Evaluation Manual (PartB, Development of Risk-based
Preliminary Remediation Goals) (U.S. EPA, 1991a; hereafter, RAGS Part B) and subsequent
modifications. For dermal pathways, which are not covered in RAGS Part B, the algorithms
presented in Dermal Exposure Assessment: Principles and Applications, Interim Report (U.S.
EPA, 1992d; hereafter, the Dermal document) were used; this document reflects the current
state-of-the-art for dermal exposure.
For lead, a different approach to setting media concentrations had to be taken because
no health benchmarks are available for lead. However, lead does have important and serious
health effects, so retaining it in the analysis was desirable if possible. Therefore, media levels
for lead in air, water, and soil were set to the following values:
• Air: 1.5 ug/m3, based on the NAAQS
• Water 0.015 mg/L, based on the Office of Water Action Level
• Soil: 400 nig/kg, based on the Superfund Soil Action Level.
Because no equivalent values were available for food products, none of the foodchain
pathways were modeled for lead.
5.2.2 Air Concentrations
Inh
Air concentrations are backcalculated based on inhalation of air by either an adult
resident or an on-site worker. Table 5-1 summarizes the backcalculated air concentrations by
chemical and receptor.
Starting with a target risk or hazSfd quotient, the concentration in air is backcalculated.
Two different algorithms are used, one for carcinogens and one for noncarcinogcns.
Equations 5-1 and 5-2 present the exposure backcalculations for air for an adult resident (for
carcinogens and noncarcinogens, respectively). Equations 5-3 and 5-4 present the same
backcalculations for an on-site worker. These differ from the adult resident only in the inputs
used.
August 1995 . 5-3
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-1. Exposure Media
.
Chemkal
Acenaphthene
Acetone
Acetonitrile
Acetophenone
Acrotein
Acrylamide
Acrylonitrile
Aldrin
Allyl chloride
Aniline
Antimony
Arsenic
Barium
Benz(a)anthracene
Benzene
Benzidine
Benzo(d)pyrene
Benzo(6)fluoranthene
Benzyl alcohol
Benzyl chloride
Beryllium
Bis(2-chloroisopropyl) ether
Bis(2-chlorethyl)ether
Bis(2-€thylhexyl)phthalate
Bromodichioromethane
Bromoform (tribromomethane)
Butanol '*»
Butyl-4,6-dinitrophenol, 2-sec- (dinoseb)
Butylbenzylphthalate
Cadmium
Carbon disulfide
i
Concentrations
CAS
83329
67641
75058
98862
107028
79061
107131
309002
107051
62533
7440360
7440382
7440393
56553
71432
92875
50328
205992
100516
100447
7440417
39638329
111444
117817
75274
75252
71363
88857
85687
7440439
75150
for Air (mg/m3)
Inhalation
Adult resident
NA
NA
5e-02
NA
2e-05
2e-06
4e-05
5e-07
le-03
le-03
NA
6e-07
5c-04
NA
3e-04
4e-08
le-06
NA
NA
NA
le-06
NA
7e-06
NA
NA
2e-03
NA
NA
NA
le-06
le-02
Worker
NA^
NA
7eX)2
NA
3e^)5
3e-06
6e-05
Se-07
le-03
lc-03
NA
le-06
7e-04
NA
5e-04
6e-08
2e-06
NA
NA
NA
2e-06
NA
le-05
NA
NA
4e-03
NA
NA
NA
2e-06
le-02
(continued)
August 1995
5-4
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Chemical
Carbon tetrachloride
Chlordane
Chloro-13-butadiene, 2- (chloroprene)
Chloroaniline, p-
Chlorobenzene
Chlocobenzilate
Chlorodibromomethane
Chloroform
Chlorophenol, 2-
Chromium VI
Chrysene
Copper
Cresol, m-
Cresol, o-
Cresol.p-
Cumene
ODD
DDE
DDT
Di-fl-butyl phthalatc
Di-n-octyi phthalate
Diallate
Dibenz(a,/i)anthracene
Dibromo-3-chloropfOpane, 1,2-
Dichlorabenzene, 1,2-
Dichlorobenzene. 1,4-
Dichlorobenzidine, 33'-
Dichlorodifluoromethane
Dichloroethane, 1,1-
Dichloroethane. 1,2-
Dichloroethylene, 1,1-
Table 5-1 (continued)
CAS
56235
57749
126998
106478
108907
510156
124481
67663
95578
7440473
218019
7440504
108394
95487
106445
98828
72548
72559
50293
84742
117840
2303164
53703
96128
95501
106467
-*» 91941
75718
75343
107062
75354
Inhalation
Adult resident
2e-04
7e-06
7e-03
NA
2e-02
NA
NA
le-04
NA
2C-07
NA
NA
NA
NA
NA
9e-03
NA
NA
3c-05
NA
NA
NA
NA
le^Ol
2e-01
8e-01
NA
2e-01
' 5e-01
9e-05
5e-05
•
Worker
3e-04
le-05
le-02
NA
3e-02
NA
NA
2c-m
NA
3C-07
NA
NA
NA
NA
NA
le-02
NA
NA
4e-05
NA
NA
NA
NA
2e+01
3e-01
16400
NA
3e-01
7e-01
2c-04
8e-05
(continued)
August 1995
5-5
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-1
Chemical
Dichloroethylene, CM- 1,2-
Dichloioethylene, trans-12-
Dichlorophenol, 2,4-
Dichlorophenoxyacetic acid, 2.4- (2,4-D)
Dichloropropane, \2-
Dichloiopropene, 13-
Dichloropropene, or- 1,3-
Dichloropropene, /ro/u-13-
Dieldrin .
Diethyl phthalate
Diethylstilbestrol
Dimethoate
Dimethyl phthalate
Dimethylbenz(a)anthracene, 7,12-
Dimethylbenzidine, 33'-
Dimethylphenol, 2,4-
Dimethyoxybenzidine, 33'-
Dinitrobenzene, 13- .
Dinitrophenol, 2,4- .
Dinitrotoluenc, 2,4-
Dinitrotoluene, 2,6-
Dioxane, 1,4-
Diphenylamine
Disulfoton
Endosulfan
Endrin
Epichlorohydrin -<^
Ethoxyethanol, 2-
Ethyl acetate
Ethyl ether
Ethyl methacrylate
(continued)
CAS
156592
156605
120832
94757
78875
542756
10061015
10061026
60571
84662
56531
60515
131113
57976
119937
105679
119904
99650
51285
121142
606202
123911
122394
298044
115297
72208
106898
110805
141786
60297
97632
Inhalation
Adult resident
NA
NA
NA
NA .
4e-03
7e-05
7e-05
7e-05
5e-07
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
2e-03
2e-01
NA
NA
NA
Worker
NA
NA
NA
NA.
6c-03
le-04
le-04
le-04
9e-07
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA -
NA
3e-03
3e-01
NA
NA
NA .
(continued)
August 1995
5-6
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
...- -
Chemical
Ethyl methanesulfonate
Ethylbenzene
Ethyleae dibromide
Ethylene Uuourea
Fluoranthene
Fluorene
Formaldehyde
Formk acid
Furan
Heptachlor
Heptachlor epoxide
Hexachloro-13-butadiene
Hexachtorobenzene
Hexachlorocyclohexane, a- (a-BHC)
Hexachkxocyclohexane. p- (0-BHQ
Hexachlorocyclohexane, j- (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
Hexachlorophene
Indcna(l23-c4) pyrene
bobutyl alcohol
T4bMkk«MMMA
L!HJ|NMJiV)l)C
Kepone
Lead
Mercury
Methaoylonitrife
Methanol
Methoxychkr
Methyl btomide (bromomethane)
Methyl chloride (chkxomethane)
Methyl ethyl ketone
-
Table 5-1 (continued)
CAS
62500
100414
106934
96457
206440
86737
50000
64186
110009
76448
1024573
87683
118741
319846
319857
58899
77474
67721
70304
193395
78831
•7OCQ1
loyti
143500
7439921
7439976
126987
-*«. 67561
72435
74839
74873
78933
Inhalation
Adult resident
NA
16400
le-05
NA
NA
NA
2e-04
NA
NA
2e-06
9e4)7
le-04
5c-06
le-06
5e-06
NA
7e^)5
6c-04
• NA .
NA
NA
KTA
NA
NA
lJe-03
3e-04
7e-04
NA
NA
5e-03
le-03
16400
•
Worker
NA
16400
2e-05
NA
NA
NA
3e-04
NA
NA
3e-06
2c-06
2e-04
9e-06
2e-06
8c-06
NA
le-04
le-03
NA
NA
NA
MA
NA
NA
1.56-03
4e-04
le-03
NA
NA
7e-03
2e-03
16400
(continued)
August 1995
5-7
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
'
... -
Chemical
Mediyl isobutyi ketone
Methyl methacrylate
"' Methyl parathion • "
Methylcholanthrene, 3-
,„ Methylene bromide.. . . ..
njtru Methylene chloride .••/_,
lateo Molybdenum
Co:v"' N-Nitrosodi-n-propylamine
/V-NittXModiphenylamine
T-SC Af-Nitrosopiperidine . .., ,,
ccunv. Af-Nitrosopynolidine
Naphthalene
' *~ Naphthylamine
Nickel
-Quii Nitrobenzene •.. ^ . . . _ ...
t;freii. Nitropropane,2- ~AT -..:•?;!.,•
faier Nitro9Odi-/i-butylamiit6
M>M:S{> Nitrosodiethylamine-: -=.-•-"- -- -«-:
Nitrosodimethylamine
..tn~ .. Nitrosomethylethylamine y,.v-
Octatnethylpyrophospnoranude
Parathion
Dtentnr>hLn>nh«HrMi*
Pentachlbrophenoi
Phenol
Phenyl mercuric acetate
Phenylenediamine, m»
Phorate
Polychlohnated biphenyls
Pronamide '
Table 5-1 (continued)
CAS
108101
80626
298000 '°
u 56495'' lC
• . - - - • - • p/-*
..... ,_ ., , 74953.- f0i
..;:, ,,,^,.75092'.,^
•rv • --r;po?r 743998T-;: .
' ••' ' 621647;-'t;-:'
: * 86306
.-'-V.i.r... '""''.,- 10075j4. ,.,-
930552 A
91203
91598
7440020
r ; ;; ^.fr~t ni -.. 794S»i./v
--. ,-••••'•-. •-'- -'•• 924163 an
,,^^;;iujf.-,,55i83;p;a
\}£ / JjT
15216*
56382
608935
82688
87865
108952
62384
108452
298022
1336363
23950585
InbahtfcM
Adult resident
8c-02
NA
n." NA
'*!" NA
DC ' NA
•':i/ 5e-03
«•• NA'
NA
'."T NA
ax.... ,:..NA
iv 4e-06
NA
NA
NA
.. , ^ 2e-03
^ .,.,.9e-0f7
c >—• 2e-06
• r^i^Jii, ^^ AQ
^^•^^ 06*4*1
"' 2e^)7
±,- -• NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Worker
le-01
NA
NA
NA.
NA
9e-03
NA
NA
NA
NA
lt-06
NA
NA
NA
3e-03
2e-06
3e-06
le-07
3e^J7
NA
NA
NA
NA
i
NA
NA
NA
NA
NA
NA
NA
NA
(continued)
August 1995
5-8
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table 5-1
Chemical
Pyrene
Pyridine
Sanxde
Selenium
Strychnine
Stryene
TCDD, 23,7,8-
Tetrachlorobenzene, 1,2,4,5-
Tetrachloroethane, 1,1.1,2-
Tetrachloroethane, 1,1,2,2-
Tetrachloroethylene
Tetrachlorophenol, 23.4,6-
Tetraethyldithiopyrophosphate
Thallium (I)
Toluene
Toluenediamine, 2,4-
Toluidine, o-
Toluidine, p-
Toxaphene
Trichloro-U^-trifluoroethane, 1,1,2-
Trichloroberizene, 1,2,4-
Trichloroethane, 1.1,1-
Trichloroethane. 1,1,2-
Trichloroethylene
Trichlorofluoromethane
Trichlorophenol. 2,4^-
Trichloropnenol, 2,4^- - ^^
Trichlorophenoxyacetk acid, 2,4X245-T)* '
Trichlorophenoxypropionk acid, 2,4^-(silvcx)
Trichloropropane, 1,2,3-
Trinitrobenzene, sym-
'
(continued)
CAS
129000
110861
94597
7782492
57249
100425
1746016
95943
630206
79345
127184
58902
3689245
7440280
108883
95807
95534
106490
8001352
76131
120821
71556
79005
79016
75694
95954
88062
93765
93721
96184
99354
Inhalation
Adult resident
NA
7e-03
NA
NA
NA
16400
5c-ll
NA
3e-04
4e-05
NA
NA
NA
NA
4e-01
NA
NA
NA
8e-06
3e401
9e-03
16400
2e-04
NA
7e-01
NA
8e-04
NA
NA
NA
NA
Worker
NA
lc-02
NA
NA
NA
16400
9e-ll
NA
6e-04
7e-05
NA
NA
NA
NA
6e-01
NA
NA
NA
le-05
46401
le-02
16400
3e-04
NA
16400
NA
le-03
NA
NA
NA
NA
(continued)
August 1995
5-9
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-1 (continued)
Inhalation
Chemical
CAS
Adult resident
Worker
Tris (2 J-dibromopropyl) phosphate
Vanadium
Vinyl chloride
126727
7440K2
75014
NA
NA
3e-05
NA
NA
Xylenes (total)
Zinc
1330207
7440666
3e-01
NA
4e-01
NA
NA - Not applicable.
August 1995
5-10
-------
5.0 EXPOSURE 5 J Concentrations for Human Receptors
For carcinogens, exposure is averaged over a lifetime (therefore, the averaging time is
70 years, the average lifetime). Risk is a function of total intake over the averaging time and
the inhalation cancer slope factor.
\
For noncarcinogens, the air concentration to which a person is exposed is
backcalculated by direct comparison to the reference concentration. The reference
concentration is the concentration to which a person may be exposed daily over a lifetime or
significant portion of a lifetime without adverse effect
August 1995 5-11
-------
5.0 EXPOSURE
53 Concentrations for Human Receptors
Air Concentration: Carcinogens — Adult Resident
c _ TR*AT»365d/yr»BW
"" IR*ED*EF.CSFinltat
Parameter
C«ir
TR
AT
BW
IR
ED
EF
CSF^
Definition
Concentration in air (mg/m3)
Target individual risk level (unitless)
Averaging time (yr)
Body weight (kg)
Inhalation rate (m3/d)
Exposure duration (yr)
Exposure frequency (d/yr)
Inhalation cancer slope factor (mg/kg/d)'1
Default
value*
io*
70
70
20
350
Central
tendency High-end
value value Refer to
Calculated
52.9.1
5.2.9.1
5.2.9.1
5.2.92
9 30 52.9.1
5.2.9.1
Chemical-specific 52.9.63
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 1991a).
-**.
August 1995
5-12
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
Air Concentration: Noncarcinogens—Adult Resident
Cair=HQ.RfC (5-2)
Central
Default tendency High-end
Parameter Definition value* value value Refer to
C~ Concentration in air (mg/m3) Calculated
HQ Target hazard quotient (unitiess) 1 52.9.1
RfC Inhalation reference concentration (mg/m1) Chemicai-specific 5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Pan B (U.S. EPA. 199 la).
August 1995 . . 5-13
-------
5.0 EXPOSURE
5 J Concentrations for Human Receptors
Air Concentration: Carcinogens—Worker
TR*AT*365d/yr*BW
IR*ED*EF*CSFinhai
(5-3)
Parameter
C*
TR
AT
BW
IR
ED
EF
CSF^
Definition
Concentration in air (mg/m3)
Target individual risk level (unitlcss)
Averaging time (yr)
Body weight (kg)
Inhalation rate (m3/d)
Exposure duration (yr)
Exposure frequency (d/yr)
Inhalation cancer slope factor (mg/kg/d)'1
Default
value"
Central
tendency
value
High-end
value
Refer to
Calcula1*^
iur*
70
70
20
250
9
25
Chemical-specific
5.2.9.1
5.2.9:1
5.2.9.1
5.2.9.2
52.9.1
52.9.1
52.9.63
* Default values are standard default used in Agency risk assessments or policy used in this rate-making.
Source: RAGS Part B (U.S. EPA, 1991a).
August 1995
5-14
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
Air Concentration: Noncarcinogens—Worker
(5-4)
Central
Default tendency High-end
Parameter Definition value* value value Refer to
C~ Concentration in air (mg/m3) Calculated
HQ Target hazard quotient (unitiessj 1 52.9.1
RfC inhalation reference concentration (mg/m3) Chemical-specific 5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 199 la).
August 1995 5-15
-------
S.O EXPOSURE
5.2 Concentrations for Human Receptors
5.2J Soil Concentrations
Soil concentrations are backcalculated based on ingestion of soil by a resident (for
carcinogens, exposure is assumed to span childhood and adulthood; for noncarcinogens, only
childhood exposure is considered) or based on dermal exposure to a child resident, an adult
resident, or an on-site worker. Table 5-2 summarizes the backcalculated soil concentrations
by chemical, exposure route, and receptor.
5.2J.I Soil Ingestion
r^
or
1<
ing
Soil
Starting with a target risk or hazard quotient, the concentration in soil is backcalculated.
Two different algorithms are used, one for carcinogens and one for noncarcinogens. The
RAGS Part B algorithm used for this analysis reflects the most recent approach to calculation
of risks from soil ingestion. Two different residential receptors are used for carcinogens and
noncarcinogens. For carcinogens, exposure in both childhood and adulthood is considered for
the same individual. For noncarcinogens, only exposure in childhood is considered.
Equations 5-5 and 5-6 present the exposure b' ^calculations for soil ingestion for a resident
(child and adult) for carcinogens and a child resident for noncarcinogens, respectively.
For carcinogens, exposure is averaged over a lifetime (therefore, the averaging time is
70 years, the average lifetime). Due to the long-term cumulative effect of carcinogens,
exposure to adults is. usually assumed. For most routes of exposure (e.g., drinking water
ingestion, air inhalation, or food ingestion), the intake rate remains proportional to body
weight throughout the lifetime, and little effect would be seen by considering child exposure.
However, the intake rate of soil is inversely proportional to body weight because children
ages 1 to 6 years are considered more likely to ingest significant quantities of soil than adults.
Therefore, exposure to contaminants via soil ingestion is calculated as a weighted average of
exposures for 6 years (ages 1 to 6) as a child and as an adult The total exposure is still
averaged over a lifetime.
For noncarcinogens, the daily intake is backcalculated by direct comparison to the
reference dose. The reference dose is the dose to which a person may be exposed daily over
a lifetime or significant portion of a lifetime without adverse effect Because the reference
dose is a daily dose, and because the daily dose from soil ingestion is likely to be consider-
ably higher for children than adults, the daily intake (and therefore soil concentration) is ,
calculated for children ages 1 to 6 years.
August 1995
5-16
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-2. Exposure Media Concentrations for Soil (mg/kg)
Chemical
Acenaphthene
Acetone
Acetonitrile
Acetophenone
Acrotein
Acrylamide
Acrykxiidile
Aldrin
Allyl chloride
Aniline
Antimony
Arsenic
Barium
Benz(a)anthracene
Benzene
Benzidine
Benzo(a)pyrene
Benzo(6)fluoranthene
Benzyl alcohol
Benzyl chloride
Beryllium
Bis(2-cWoroisopropyl) ether
Bis(2-chloremyl)emer
Bis(2-ethylhexyl)phtnalate
Bromodichlorotnethane
Bromofonn (tribromomethane)
Butanol
Butyl-4,6-dinitrophenol, 2-sec- (dinoseb)
Butylbenzylphthalate
Cadmium
Carbon disulfide •
CAS
83329
67641
75058
98862
107028
79061
107131
309002
107051
62533
7440360
7440382
7440393
56553
71432
91-75
50328
205992
100516
100447
7440417
39638329
111444
117817
75274
75252
71363
88857
85687
7440439
75150
Ingestioo
Resident
56403
86403
56402
86403
26403
le-01
16400
4e-02
NA
16402
36401
4e-01
56403
6e-01
26401
3e-03
9e-02
5e-01
26404
46400
le-01
96400
6e-01
56401
16401
86401
86403
86401
26404
86401
86403
Adult
resident
96404
16407
76405
36405
56406
4e-02
8e402
6e-02
NA
66402
36401
2e-01
76404
26-01
46404
7e-04
2e-02
2e-01
16405
66402
96-02
46402
46401
36401
36404
16405
66405
76404
26404
76403
26408
Dermal
Child
resident
36404
56406
2e405
16405
26406
7e-02
16403
le-01
NA
16403
16401
66-01
76404
3e-01
76404
le-03
4e-02
4e-01
36404
16403
4e-01
66402
66401
16402
66404
26405
26405
26404
96403
26403
66407
Worker
36405
46407
26406
16406
16407
le-01
36403
2e-01
NA
26403
96401
Se-01
26405
6e-01
16405
3e-03
8e-02
7e-01
36405
26403
3e-01
16403
16402
16402
16405
4e405
2e406
2e405
7e404
26404
56408
(continued)
August 1995
5-17
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-2 (continued)
Chemical
Carbon tetrachloride
Chtordane
Chloro-l,3-butadiene, 2- (chlorpprene)
Chloroaniline, p-
Chlorobenzene
ChJorobenzilate
Chlorodibromomethane
Chloroform
ChlorophenoU 2-
Chromium VI
Chrysene
Copper
Cresol, m-
Cresol, 0-
Cresol, p- •
Cumene
DDD
DDE
DDT
Di-n-butyl phthalate
Di-n-octyl phthalate
Diallate
Dibenz(a,/0anthiacene
Dibromo-3-chloropropane, 1,2-
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,4-
Dichlorobenzidine, 33'-
Dichlorodifluoromethane
Dichloroethane, 1,1-
Dichloroethane, 1,2-
Dichloroethylene, 1,1-
CAS
56235
57749
126998
106478
108907
510156
124481
67663
95578
7440473
218019
7440504
108394
95487
106445
9oo28
72548
72559
50293
84742
117840
2303164
53703
96128
95501
^ 106467
91941
75718
75343
107062
75354
Ingestioa
Resident
5C400
5e-01
NA
3c-f02
2e403
2C400
86400
16402
46402
46402
26401
36403
46403
46403
46402
36403
36400
26400
26400
86403
26403
16401
Se-02
5e-01
76403
36401
16400
26404
76400
76400
16400
Adult
resident
.. 56404
16400
NA
16405
36406
7e-01
36404
46405
16404
46402
56400
36403
16404
26404
16403
86406
9e-01
26400
26400
96403
56403
76400
2e-02
26402
46406
66403
4e-01
• 7e+10
36404
16404
16404
Dermal
Child
resident
86404
26400
NA
46404
1*406
16400
66404
76405
56403
36402
96400
2&403
46403
56403
36402
36406
26400
36400
46400
36403
56403
16401
5e-02
36402
16406
16404
76-01
364-10
56404
26404
26404
Worker
26405
46400
NA
36405
96406
26400
16405
16406
46404
16403
26401
96403
36404
56404
36403
26407
36400
56400
66400
36404
26404
26401
Se-02
66402
16407
26404
16400
264-11
16405
jCrO^
46404
(continued)
August 1995
5-18
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-2 (continued)
Chemical
Dichloroethylene, m-1,2-
Dichloroethylene, trans-13-
Dichlorophenol, 2,4-
DichkMophenoxyacetic acid, 2,4- (2,4-D)
Dtchloropropane. 1,2-
Dichloropropene, 13-
Dichioropropene, ci'j-13-
Dichloropropene, trans-l3~
Dieldrin
Diethyl phthalate
Diethylstilbestrol
Dimethoate
Dimethyl phthalate
Dimethylbenz(a)anthracene, 7,12-
Dimethylbenzidine, 33'-
Dimethylphenol, 2,4-
Dimethyoxybenzidine, 33'-
Dinitrobenzene, 1,3-
Dinitrophenol, 2,4-
Dinitrotoluene, 2,4-
Dinitrotoluene, 2,6-
Dioxane, 1,4-
Diphenylamine
Disulfoton
Endosulfan
Endrin
EpichlOTohydrin
Ethoxyethanol, 2-
Ethyl acetate
Ethyl ether
Ethyl methacrylate
CAS
156592
156605
120832
94757
78875
542756
10061015
10061026
60571
84662
56531
60515
131113
57976
119937
1^-579
119904
99650
51285
121142
606202
123911
122394
298044
115297
72208
'** 106898
110805
141786
60297
97632
Ingestioo
Resident
8e+02
2e+03
26402
8e+02
96400
46400
46400
46400
4c-02
66404
le-04
26401
86405
36-02
7e-02
26403
56401
86400
2&402
26402
86401
66401
26403
36400
56402
26401
66401
36404
76404
26404
76403
Adult
resident
86406
1C407
36402
9e403
26404
66403
4e403
36403
2e-02
2e405
4c-05
16401
66406
7e-03
2e-02
86403
16401
46401
26402
56402
16402
26404
46403
26401
56404
46401
26404
86410
16408
16408
46406
Dermal
Child
resident
36406
5e406
96401
36403
3e404
16404
76403
66403
Se-02
76404
6e-05
56400
26406
le-02
3e-02
36403
26401
16401
56401
26402
5C401
36404
16403
66400
26404
26401
36404
36410
56407
46407
16406
Worker
26407
46407
86402
36404
66404
26404
16404
16404
8e-02
66405
le-04
46401
26407
2e-02
66-02
26404
46401
16402
5e402
16403
46402
66404
16404
56401
16405
16402
6e404
2e+U
4e408
36408
16407
• ' • (continued)
August 1995
5-19
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-2 (continued)
Chemical
Ethyl methanesulfonate
Ethylbenzene
Ethylenc dibromide
Ethytene thiourea
Fluoranthene
Fluorene
Formaldehyde
Formic acid
Furan
Heptachlor
Heptachlor epoxide
Hexachloro-l,3-butadiene
Hexachlorobenzene
Hexachlorocyclohexane. a- (a-BHC)
Hexachlorocyclohexane, ft- (0-BHQ
Hexachlorocyclohexane, f- (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
Hexachlorophene
Indeno(l^J-c^) pyrene
Isobutyl alcohol
Isophorone
Kepone
Lead
Mercury
Melhacrylonitrife
Methanol
Methoxychlor
Methyl bromide (bromomethane)
Methyl chloride (chloromethane)
Methyl ethyl ketone
CAS
62500
100414
106934
96457
206440
86737
50000
64186
110009
76448
1024573
. 87683
118741
319846
319857
5C-^9
77474
67721
70304
193395
78831
78591
143500
7439921
7439976
126987
"*Bi
- 67561
72435
74839
74873
78933
Ingestioo
Resident
2e-03
8e-K)3
8e-03
16400
36403
36403
26404
26405
86401
le-01
7e-02
86400
4e-01
le-01
4e-01
5e-01
56402
56401
26401
26400
26404
76402
lc-02
46402
26401
86400
46404
46402
16402
NA
56404
Adult
resident
4e-03
26407
96400
46401
46403
36404
26408
2C407
26406
26400
le-01
76403
36400
3e-01
le-01
9e-01
56405
,76402
76401
4e-01
36406
56403
le-02
NA
56404
26404
76408
16403
16407
NA
36407
Dermal
Child
resident
7e-03
6e406
26401
76401
16403
16404
6e407
. 76406
66405
36400
2e-01
16404
56400
5e-01
2e-01
16400
26405
16403
76401
16400
16406
86403
5e-02
NA
56404
8e403
36408
4e402
36406
NA
16407
Worker
le-02
56407
36401
16402
16404
9e404
56408
66407
56406
76400
5e-01
26404
16401
16400
5e-01
36400
26406
26403
26402
26400
9e406
2e404
3e-02
' NA
2e405
76404
26409
46403
36407
NA
16408
(continued)
August 1995
5-20
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-2 (continued)
Chemical
Methyl isobutyl ketone
Methyl methacrylate
Methyl parathion
Methylcholanthiene, 3-
Methylene bromide
Methylene chloride
Molybdenum
Af-Nitrosoditfl-propylainiiie
Af-Nitrosodiphenylamine
tf-Nitrosopiperidine
tf-Nitrosopynolidine
Naphthalene
Naohthylamine
Nickel
Nitrobenzene
Nitropropane, 2-
Nitrosodi-n-butylamine
Nitrosodiethylamine
Nitrosodimethylamine
Nitrosomethylethyiamine
Octamethylpyrophosphoramide
Parathion
Pentachlocobenzene
PeniachloronitFobenzene (PCNB)
Pentachlorophenol
Phenol
Phenyl mercuric acetate
Phenylenediamine, m-
Phorate
Polychlorinated biphenyls
Pronamide
CAS
108101
80626
298000
56495
74953
75092
7439987
621647
86306
100754
930552
91203
91598
7440020
98953
7*»69
924163
55185
62759
10595956
152169
56382
608935
82688
87865
^ 108952
' 62384
108452
298022
1336363
23950585
Ingestioo
Resident
46403
66403
26401
3e-02
8e402
96401
46402
9e-02
16402
26-02
3e-01
36403
NA
2e403
46401
NA
le-01
4e-03
le-02
3e-02
26402
56402
66401
26400
56400
56404
66400
56402
26401
Se-02
66403
Adult
resident
. 36406
16407
36401
7e-03
76406
46405
46402
86400
36404
2e-02
2e-01
36405
NA
46403
26403
NA
46400
4e-02
2e-01
4e-01
26402
86402
36403
86403
36400
26405
66400
66402
26402
le-01
86404
Dermal
Child
resident
16406
36406
96400
le-02
26406
66405
36402
16401
56404
36-02
4e-01
16405
NA
46403
86402
NA
66400
66-02
3e-01
6e-01
86401
36402
16403
16404
56400
56404
26400
26402
66401
2e-01
36404
Worker
96406
36407
86401
2e-02
26407
16406
16403
36401
16405
7e-02
9e-01
96405
NA
16404
76403
NA
16401
le-01
6e-01
16400
76402
26403
9C403
36404
16401
46405
26401
26403
56402
4e-01
26405
• (continued)
August 1995
5-21
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-2 (continued)
Cbemkal
Pyrene
Pyridine
Safrole
Selenium
Strychnine
Stryene
TCDD, 2.3,7,8-
Tetrachlofobenzene. 1,2,4,5-
Tetrachloroethane, 1,1,U-
Tetrachloroethane, U.2,2-
Tefrachloroethylene
Tetrachlorophenol, 2.3,4,6-
TetraeChyldithiopyrophosphate
Thallium (I)
Toluene
Toluenediamine, 2,4-
Toluidine, o-
Toluidine, p-
Toxaphene
Trkhloro-l,2.2-trifluoroethane, 1,1,2-
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
, Trichloroethane. 1,1,2-
Trichloroethylene
Trichlorofiuoromethane
Trichlorophenol, 2,4,5- ^
TrichlorophenoK 2,4^5-
Trichlorophenoxyacetic acid, 2,4.5- (245-T)
Trichlorophenoxypropiofuc acid, 2,4,5- (silvex)
Trichloropropane. 1^3-
Trinilrobenzene, synt-
CAS
129000
110861
94597
7782492
57249
100425
1746016
95943
630206
79345
127184
58902
3689245
7440280
•108883
9:_J7
95534
106490
8001352
76131
120821
71556
79005
79016
75694
95954
88062
93765
93721
96184
99354
logestioo
Resident
2e^03
86401
46400
46402
26401
26404
4e-06
26401
26401
36400
86402
26403
46401
66400
26404
2e-01
36400
36400
66-01
26406
86402
MA
16401
66401
26404
86403
66401
86402
66402
56402
46400
Adult
resident
36403
76406
26401
46402
26401
264O7
2e-05
36403
26404
76402
26407
16404
46402
16401
56407
66-02
16401
36401
4e-01
164-12
46405
NA
86403
26404
46409
26404
46401
764O2
66402
46405
16401
Dermal
Child
resident
16403
26406
36401
16402
76400
66406
3e-05
16403
46404
16403
66406
46403
16402
16401
2e4O7
le-01
26401
56401
16400
464-11
16405
NA
16404
46404
16409
66403
86401
26402
26402
16405
56400
Worker
96403
26407
76401
16403
66401
56407
7e-05
16404
96404
36403
56407
36404
16403
46401
16408
2e-01
46401
16402
26400
364-12
16406
NA
36404
86404
164-10
56404
26402
26403
26403
16406
46401
(continued)
August 1995
5-22
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table 5-2 (continued)
Chemical
agestioo
CAS Resident
Dermal
Adult
resident
Child
resident
Worker
Tris (23-dibromopropyl) phosphate
Vanadium
Vinyl chloride
126727
7446622"
75014"'
7e-02
46402 66402 16403
Q*u_fY) &AA/ft ' I* * Ai
Jb f \M> UV WM JvTVJ
36404 4C404 96404
3e4M
Xylenes (total)
Zinc
1330207
7440666
3e*08
8e-»08
•-—-
3e+04
NA = Not applicable.
August 1995
5-23
-------
5.0 EXPOSURE
5J2 Concentrations for Human Receptors
Soil Concentration: Carcinogens—Child and Adult
TR*AT'365d/yr'103mg/g
Parameter
C,ou
TR
AT
EF'
KCWM
EDchM
BWchild
nU»
ED«l«h
BW^
CSForal
pp . JR child * ED child ^ IR adult ' E D adult
D\lf D\t/
DW child aw adult
(5-5)
•CSForal
Central
Default tendency High-end
Definition . value* value value Refer to
Concentration in soil (mg/g)
Target individual risk level (unitless) 10~*
Averaging time (yr) 70
Exposure frequency (d/yr) 350
Soil ingestion rate for children (tng/d) 200
Exposure duration for children (yr) 6
Body weight for children (kg) 15
Soil ingestion rate forfadults (mg/d) 100
Exposure duration for adults (yr)
Body weight for adults (kg) 70
Calculated
5.2.9.1
52.9.1
5.2.9.1
52.92
52.9.1
52.9.1
52.92
3 24 52.9.1
52.9.1
Oral cancer slope factor (mg/kg/d)"1 Chemical-specific 52.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 199 la).
•*s»
August 1995
5-24
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Parameter
C,oa
HQ
RID
BWchild
»duk.
Soil Concentration:
HQ
r , = . „
Definition
Concentration in soil (mg/g)
Target hazard quotient (unitless)
Oral reference dose (mg/kg/d)
Body weight for children (kg)
Soil ingesn'on rate for children (r
Noncarcinogens — Child Resident
•RfD'BW^ltfmg/g
I^chM
Central
Default tendency High-end
value* value value
Calculated
1
Chemical-specific
15
ng/d) 200
(5-6)
Refer to
5.2.9.1
52.9.63
52.9.1
52.92
4 Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 1991a).
August 1995
5-25
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
5.2J.2 Dermal Soil Exposure
^ ^
or
1<
or
Starting with a target risk or hazard quotient, the concentration in contaminated soil to
which humans are dermally exposed is backcalculated in three steps. First, the daily average
lifetime dose associated with the target risk or hazard quotient is backcalculated. Two
different algorithms are used to backcalculate daily average lifetime dose, one for carcinogens
and one for noncarcinogens. Second, the absorbed dose per exposure event is then back-
calculated from the average daily lifetime dose. Again, two different algorithms are used to
backcalculate absorbed dose per exposure event—one for carcinogens and one for noncarcino-
gens. Finally, the concentration in soil is backcalculated from the absorbed dose per event
Dermal soil exposures for residents are calculated for both adults and children. Adults
have a longer exposure duration, but children have a greater surface area to body weight ratio
and, hence, higher dermal exposure than adults. This ratio decreases consistently with age;
therefore, a 1- to 6-year-old child was used to reflect the highest surface area to body weight
ratio. Dermal soil exposures were calculated separately for children and adults.
For an adult resident, Equations 5-7, 5-8, and 5-9 show the backcalculation of daily
average dose for most carcinogens, with modifications for dioxin-like compounds, and for
noncarcinogens, respectively. Equations 5-10 and 5-11 show the backcalculation of absorbed
dose per event for carcinogens and noncarcinogens, respectively. Equation 5-12 shows the
backcalculation of soil concentration, and Equations 5-13 through 5-16 show the calculation
of several inputs to Equation 5-12. Equations 5-17 through 5-26 present the same
backcalculations for a child resident, and Equations 5-27 through 5-36 present the same
calculations for a worker. These differ from the adult resident only in the inputs used.
5.2 J.2.1 Daily Average Lifetime Dose
For carcinogens, daily average lifetime dose is a function of the target risk and the
cancer slope factor. Because appropriate4cancer slope factors for dermal exposure arc not
available, oral cancer slope factors are used, as recommended by EPA in the Dermal
document The equation was modified slightly for dioxins and PCBs to include a correction
factor that adjusts the oral cancer slope factor to an absorbed basis (Dioxin document U.S.
EPA, 1992c).
August 1995
5-26
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
For noncarcinogens, daily average dose is a function of target hazard quotient and the
reference dose. Because appropriate reference doses for dermal exposure are not available,
oral reference doses are used, as recommended by EPA in the Dermal document
5.2J.2.2 Absorbed Dose per Event
For carcinogens, exposure is averaged over a lifetime (therefore, the averaging time is
70 years, the average lifetime). Absorbed dose per event is backcalculated from the daily
average lifetime dose, body weight, skin surface area, and exposure parameters such as the
event frequency, exposure frequency, and duration.
For noncarcinogens, the absorbed dose per event is backcalculated from the daily
average dose, body weight, skin surface area, and the daily.event frequency. Because the
daily average dose is based on a reference dose, which is the dose to which a person may be
exposed daily over a lifetime or significant portion of a lifetime without adverse effect,
exposure duration, frequency, and averaging time are not included in the calculation.
5.2J.2.3 Soil Concentration
Soil concentration and dose absorbed per event are related by an adherence factor,
which reflects how well soil adheres to skin, and an absorption fraction, which reflects the
fraction of the applied dose that is absorbed. The duration of the event, t^^, is the time that
soil remains in contact with the skin. The adherence factor, AF, is an experimentally derived
value. The adherence studies are independen* ~>f time; therefore, the Dermal document
recommends interpreting the adherence factor on an event basis. The skin permeability
factor, KpS, reflects how easily constituents in soil are absorbed through the skin. The rate
constant for disappearance from soil, k^y, reflects the loss of constituent from the soil by
absorption through the skin, ensuring that mass balance of the constituent is maintained. The
rate of volatilization, k^,, reflects the loss of constituent from the soil by volatilization before
it can be absorbed through the skin.
August 1995 , 5-27
-------
5.0 EXPOSURE
SJ Concentrations for Human Receptors
Daily Average Lifetime Dose via Dermal Contact with Soil:
Carcinogens—Adult Resident
DAD
TR
CSForal
(5-7)
The above equation was modified slightly for dioxins and PCBs. The correction
factor adjusts the oral cancer slope factor to an absorbed basis (Dioxin document, U.S.
EPA, 1992c).
TR
(5-8)
Parameter
DAD
TR
CSForal
1.82
^f&tOiGil &«o^
Default
Definition value*
Daily average dose (mgAcg/d)
Target individual lifetime risk (unitless) 10*
Oral cancer slope factor (mg/kg/d)"1
Correction factor for dioxins and dioxiii-iike
compounds (unitless)
Central
tendency High-end
value value
Ca
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
. Daily Average Dose via Dermal Contact with Soil:
Noncarcinogens — Adult Resident
DAD = HQ'RfD
Default
Parameter Definition value*
DAD . Daily average dose (mg/kg/d)
HQ Target hazard quotient (unitless) 1
RfD Reference dose (mg/kg/d)
Central
tendency High-end
value value
Calculated
Chemical-specific
(5-9)
Refer to
5.2.9.1
5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-29
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
V
Parameter
DA^
DAD
AT
BW
EV
EF
ED
A
Absorbed Dose per Event for Dermal Contact with Soil:
Carcinogens — Adult Resident
nA _ DAD*AT»365d/yr*BW
"m EV'EF»ED»A
Central
Default tendency High-end
Definition value* value value
Dose absorbed per unit area per event Calculated
(mg/cm2/event)
Daily average dose (mg/kg/d) From Equations 5-7
and 5-8
Averaging time (yr) 70
Body weight (kg) 70
. Event frequency (events/d) . 1
Exposure frequency (d/yr) 40 350
Exposure duration (yr) 9 30
Exposed skin surface area (cm2)6 . 5,000
(5-10)
Refer to
52.9.1
5.2.9.1
52.9.4
52.9.1
5.2.9.1
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Skin surface area is not varied due to its relationship to body weight, for which a default value is used
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-30
-------
5.0 EXPOSURE
5 j Concentrations for Human Receptors
Absorbed Dose per Event for Dermal Contact with Soil:
Noncarcinogens — Adult Resident
DAD'BW
«
Parameter
DA_
DAD
RW
EV .
A
Definition
Dose absorbed per unit area per event
(mg/cm2/event)
Daily average dose (mg/kg/d)
Body weight (kg)
Event frequency (events/d)
Exposed skin surface area (cm2)b
GV.A
Central
Default tendency High-end
value* value value
Calculated
From Equation 5-9
70
1
5,000
Refer to
52.9.1
5.2.9.4
52.9.4 .
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Skin surface area is not varied due to its relationship to body weight, for which a default value is used.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-31
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Soil Concentration for Dermal
r '."-'
Ml Ap
Parameter Definition
C^y Soil concentration (mg/kg)
DAeva« Dose absorbed per unit area per event
(mg/cm2)
AF Adherence factor (mg/cm2)
ABS Absorption traction (unitless)
Contact— Adult Resident
>106 mg/kg
•ABS
Default
value*
Central
tendency High-end
value value
Calculated
From Equations 5-10
and 5-11
02 1.0
See Equation 5-13
(5-12)
Refer to
5.2.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-32
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Dermal Absorption Fraction—Adult Resident
ABS =
(**>,/+**>/)
(5-13)
Parameter
ABS
AF
Piod
V
"W
kvd
Urn
Definition
Absorption fraction (unitless)
Adherence factor (mg/cm2)
Particle density of soil (g/cm3)
Skin permeability constant for soil (cm/h)
Rate constant for disappearance from soil
(h-1)
Rate of volatilization from soil (h*1)
Duration of event (h)
Central
Default tendency High-end
value* value value
Calculated
02 1.0
2.65
See Equation 5-14
See Equation 5-15
See Equation 5-16
8
Refer to
52.9.4
52.9.4
5.2.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-33
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Skin Permeability Constant for Soil—Adult Resident
(5-14)
Parameter Definition
K£ Skin permeability constant for soil (cm/h)
K* Skin permeability constant for water (cm/h)
Kd Soil- water partition coefficient (cm3/g).
P.OJJ Particle density of soil (g/cm3)
Central
Default tendency High-end
value* value value
Calculated
Chemical-specific
Chemical-specific
2.65
Refer to
5.2.9.6.2
5.2.9.6.2
5.2.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
\ ' .'• '
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-34
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
Soil Disappearance Rate Constant— Adult Resident
.
"soil ~
(5.15)
Parameter
*-
K;
P»u
AF
Definition
Rate constant for disappearance from soil .
(h*1)
Skin permeability constant for soil (cm/h)
Particle density of soil (g/cm3)
Adherence factor (mg/cm2)
Central
Default tendency High-end
value* value value
Calculated
From Equation 5-14
2.65
0.2 1.0
Refer to
5.2.9.4
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
-*»
August 1995
5-35
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Parameter
k.
^Ol
H'
D*
1
AF
*d
Rate of Volatilization from
tf'D^. 3,600
AF*Kd*l*
Dennition
Rate of volatilization from soil (h"1)
Unitless Henry's law constant
.
Diffusivity in air (cnr/s)
Thickness of skin-air boundary layer (cm)
Adherence factor (mg/cm2)
Soil-water partition coefficient (LAg)
Soil — Adult Resident
s/h*lQ6mg/kg
\03cm3/L
\
Central
Default tendency High-end
value* value value
Calculated
Chemical-specific
Chemical-specific
0.5
02 1.0
Chemical-specific
(5-16)
Refer to
5.2.9.6.1
5.2.9.6.1
52.9.4
52.9.4
5.2.9.6.1
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-36
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Daily Average Lifetime Dose via Dermal Contact with Soil:
Carcinogens—Child Resident
DAD
TR
CSForal
(5-17)
The above equation was modified slightly for dioxins and PCBs. The correction
factor adjusts the oral cancer slope factor to an absorbed basis (Dioxin document, U.S.
EPA, 1992c).
TR
(5-18)
Parameter
DAD
TR
CSForal
1.82
CSForal • 1.82
Default
Definition . value*
Daily average dose (mg/kg/d)
Target individual lifetime risk (unitless) 10"*
Oral cancer slope factor (mg/kg/d)"1
Correction factor for dioxins and dioxin-like
compounds (unitless)
Central
tendency High-end
value value
Calculated
Chemical-specific
Refer to
5.2.9.1
5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (US. EPA, 1992d).
August 1995
5-37
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Daily Average Dose via Dermal Contact with Soil:
Noncarcinogens— Child Resident
DAD - HQ'RfD
Default
.Parameter Definition value*
DAD Daily average (lose (mg/kg/d)
HQ Target hazard quotient (unitless) 1
RfD Reference dose (mg/kg/d)
Central
tendency High-end
value value
Calculated
Chemical-specific
(5-19)
Refer to
5.2.9.1
5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this nile-making.
Source: Dermal document (U.S. EPA, 1992d).
August 199S
5-38
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Absorbed Dose per Event for Dermal Contact with Soil:
Carcinogens—Child Resident
DA =
-.36S*r.«ir
""" EV*EF*ED'A
Parameter
DA-
DAD
AT
BW
EV
EF
ED
A
Definition
Dose absorbed per unit area per event
(mg/cm2/event)
Daily average dose (mg/kg/d)
Averaging time (yr)
Body weight (kg)
Event frequency (events/d)
Exposure frequency (d/yr)
Exposure duration (yr)
Exposed skin surface area (cm2)6
Central
Default tendency High-end
value* value value
Calculated
From Equations 5-17
and 5-18
70
15
1
130 350
6
1,700
(5-20)
Refer to
i
52.9.1
52.9.1
52.9.4
5.2.9.1
52.9.1
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Skin surface area is not varied due to its relationship to body weight, for which a default value is used.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-39
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
Absorbed Dose per Event for Dermal Contact with Soil:
Noncarcinogens — Child Resident
DAD'BW
Parameter
DA-
DAD
BW
EY
A
Definition
Dose absorbed per unit area per event
(mg/cm2/event)
Daily average dose (mg/kg/d)
Body weight (kg)
Event frequency (events/d)
Exposed skin surface area (cm2)6
EV*A-
Central
Defauh tendency High-end
value* value value
Calculated
From Equation 5-19
15
1
1.700
Refer to
52.9.1
52.9.4
52.9.4
• Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Skin surface area is not varied due to its relationship to body weight, for which a default value is used.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5^0
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
Soil Concentration for Dermal Contact— Child Resident
(5-22)
Parameter
c
•oil
DA~-
AF
ABS
AF'ABS
Default
Definition value*
Soil concentration (mg/kg)
Dose absorbed per unit area per event
(mg/cm2)
Adherence factor (mg/cm2)
Absorption fraction (unitless)
Central
tendency High-end
value value
rak-utat"*
From Equations 5-20
and 5-21
0.2 1.0
See Equation 5-23
Refer to
5.2.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-41
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Dermal Absorption Fraction—Child Resident
-e
mg/g
. (5-23)
Parameter
ABS
AF
P«d
V
k«Hi
kvo.
ncvcuft
Definition
Absorption fraction (unitless)
Adherence factor (mg/cm2)
Particle density of soil (g/cm3)
Skin permeability constant for soil (cm/h)
Rate constant for disappearance from soil
(h-1)"
Rate of volatilization from soil (h"1)
Duration of event (h)
Central
Defauh tendency High-end
value* value value
Calculated
0.2 1.0
2.65
See Equation 5-24
See Equation 5-25
See Equation 5-26
5 12
Refer to
52.9.4
5.2.9.4
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-42
-------
5.0 EXPOSURE
5 J Concentrations for Human Receptors
Skin Permeability Constant for Soil— Child Resident
(5-24)
Parameter
K;
KP
Kd
Pwil
Definition
Skin permeability constant for soil (cm/h)
Skin permeability constant for water (cm/h)
Soil-water partition coefficient (cm3/g)
Particle density of soil (g/cm3)
Default
value*
2.65
Central
tendency High-end
value value
Calculated
Chemical-specific
Chemical-specific
Refer to
52.9.62
52.9.62
5.2.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-43
-------
5.0 EXPOSURE 5 J Concentrations for Human Receptors
Soil Disappearance Rate Constant—Child Resident
, '^ (5-25)
' - -
** AF
Central
Default tendency High-end
Parameter Definition . value* value value Refer to
kjoj, Rate constant for disappearance from soil Calculated
Of1) '
K! Skin penneability constant for soil (cm/h) From Equation 5-24
Particle density of soil (g/cm3) 2.65 • ' ' 52.9.4
AF i Adherence factor (mg/cm1) _ . 02 1.0 52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995 5-44
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Parameter
kvd
H'
®ia
1
AF
*d
Rate of Volatilization
•• > >">*
*vol
Definition
Rate of volatilization from soil (h*1)
Unitless Henry's law constant
«
Diffusivity in air (cnr/s)
Thickness of skin-air boundary layer
Adherence factor (mg/cm2)
Soil-water partition coefficient (Ukg]
from Soil— Child Resident
•WWs/h*itfmg/kg
•K^vf^lL
Central
Default tendency High«end
value* value value
Calculated
Chemical-specific
Chemical-specific
(cm) 0.5
02 1.0
I Chemical-specific
(5-26)
Refer to
5:2.9.6.1
5.2.9.6.1
52.9.4
52.9.4
52.9.6.1
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-45
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Daily Average Lifetime Dose via Dermal Contact with Soil:
Carcinogens—Worker
DAD
TR
CSForal
(5-27)
The above equation was modified slightly for dioxins and PCBs. The correction
factor adjusts the oral cancer slope factor to an absorbed basis (Dioxin document, U.S.,
EPA, 1992c).
DAD =
TR
CSForal' 1,82
(5-28)
Parameter
DAD
TR
CSForal
1.82
Definition
Daily average dose (mg/kg/d)
Target individual lifetime risk (unitless)
Oral cancer slope factor (mg/kg/d)*1
Correction factor for dioxins and dkw~ "ike
compounds (unitless)
Default
value*
10-6
Central
tendency High-end
value value
Calculated
Chemical-specific
Refer to
5.2.9.1
52.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-46
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
-\
Daily Average Dose via Dermal Contact
Noncarrinogens— Worker
Parameter
DAD
HQ
RfD
DAD =
Definition
Daily average dose (mg/kg/d)
Target hazard quotient (unitless)
Reference dose (mg/kg/d)
HQ'RfD
Default
value*
1
• ;•
with Soil:
(5-29)
Central
tendency High-end
value value Refer to
Calculated
5.2.9.1
Chemical-specific 5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rale-making.
Source: Dermal document (US. EPA, 1992d).
August 1995
5-47
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Absorbed Dose per Event for Dermal Contact with Soil:
Carcinogens— Worker
PA DAD'AT*365d/yr*BW (SmVX\
"^ EV»EF*ED'A
Parameter
DA-
DAD
AT
BW
EV
EF
ED
A
Definition
Dose absorbed per unit area per event
(mg/cm2/event)
Daily average dose (mg/kg/d)
Averaging time (yr)
Body weight (kg)
Event frequency (events/d)
Exposure frequency (d/yr)
Exposure duration (yr)
Exposed skin surface area (cm2)b
Central
Default tendency High-end
value* value value Refer to
V v
From Equations 5-27
and 5-28
70 52.9.1
70 52.9.1
1 52.9.4
250 52.9.1
9 25 52.9.1
3200 52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rale-making.
b Skin surface area is hot varied due to its relationship to body weight, for which a default value is used.
Source: Dermal document (U.S. EPA. 1992d).
August 1995
5-48
-------
5.0 EXPOSURE
Concentrations for Human Receptors
Absorbed Dose per Event for Dermal Contact with Soil:
Noncarcinogens — Worker
n/ .DAD.BW
evaa EV*A
Central
Default tendency High-end
Parameter Definition value* value value
D Aevo» Dose absorbed per unit area per event Calculated
(mg/cm2/event)
DAD Daily average dose (mg/kg/d) From Equation 5-29
BW Body weight (kg) 70
EV Event frequency (events/d) 1
A Exposed skin surface area (cm2)b 3200
(5-31)
Refer to
52.9.1
52.9.4
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Skin surface area is not varied due to its relationship to body weight, for which a default value is used.
Source: Dermal document (U.S. EPA, 1992d).
-•*»
August 1995
5-49
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
Soil Concentration for Dermal Contact—Worker
(5-32)
AF'ABS
Parameter
C«xl
AF
ABS
Definition
Soil concentration (mg/kg)
Dose absorbed per unit area per event
(mg/crh2)
Adherence factor (mg/cm2)
Absorption traction (unitless)
Central
Default tendency
value* value
Calc«
High-end
value
dated
p
Refer to
From Equation 5-31
02
1.0
52.9.4
See Equation 5-33
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-50
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Dermal Absorption Fraction — Worker
ABS =
(5-33)
Parameter
ABS
AF
P»u '
V
**
kv*
U*
Definition
Absorption fraction (unitless)
Adherence factor (mg/cm2)
Particle density of soil (g/cm3)
Skin permeability constant for soil (cm/h)
Rate constant for disappearance from soil
(h-1)
Rate of volatilization from soil (h'1)
Duration of event (h)
Central
Default tendency High-end
value* value value
Calculated
02 1.0
2.65
See Equation 5-34
See Equation. 5-35
See Equation 5-36
8
Refer to
52.9.4
5.2.9.4
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-51
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Skin Permeability Constant for Soil— Worker
(5-34)
Parameter
K;
KP
*d
Ptoil
' VP-otf
Default
Definition value*
Skin permeability constant for soil (cm/h)
Skin permeability constant for water (cm/h)
Soil-water partition coefficient (cm3/g)
Particle density of soil (g/cm3) 2.65
Central
tendency High-end
value value
Calculated
Chemical-specific
Chemical-specific
Refer to
52.9.62
52.9.62
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
»
Source: Dermal document (U.S. EPA, 1992d). '
August 1995
5-52
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Soil Disappearance Rate Constant — Worker
AF
(5-35)
Parameter
Definition
Central
Default tendency High-end
value* value value Refer to
Rate constant for disappearance from soil
Calculated
Skin permeability constant for soil (cm/h)
Particle density of soil (g/cm3)
From Equation 5-34
2.65
5.2.9.4
AF
Adherence factor (mg/cm2)
1.0
55.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (US. EPA, 1992d).
August 1995
5-53
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Rate of Volatilization from Soil—Worker
.D^ 3,600 s/h'l06mg/kg
Parameter
*»*
H'
D«w
f
AF
Kd
^'W
Definition
Rate of volatilization from soil (h*1)
Unitless Henry's law constant
.
Diffusivity in air (cnr/s)
Thickness of skin-air boundary layer (cm)
Adherence factor (mg/cm2)
Soil-water partition coefficient (Meg)
vfcm*IL
Central
Default tendency High-end
value* value value
Calculated
Chemical-specific
Chemical-specific
0.5
0.2 1.0
Chemical-specific
Refer to
52.9.6.1
52.9.6.1
5.2.9.4
52.9.4
52.9.6.1
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (US. EPA, 1992d).
August 1995
5-54
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
5.2.4 Surface Water and Groundwater Concentrations
Water concentrations (both surface water and groundwater) are backcalculatcd based on
ingestion of water by an adult resident or based on dermal exposure while bathing to either a
child resident or an adult resident Table 5-3 summarizes the backcalculated water
concentrations by chemical, exposure route, and receptor.
5,2.4.1 Water Ingestion
r .^
t
Ina
DW
Starting with a target risk or hazard quotient, the concentration in either surface or
groundwater used as drinking water is backcalculated in two steps. First, intake associated
, with the target risk or hazard quotient is backcalculated. Two different algorithms are used to
backcalculate intake, one for carcinogens and one for noncarcinogens. The water concentra-
tion is then backcalculated from the intake. Intake refers to the quantity of contaminant
ingested daily, normalized by body weight Equations 5-37 and 5-38 show the backcalcula-
tions for intake for carcinogens and noncarcinogens, respectively. Equation 5-39 shows the
backcalculation for water concentration from ' .lake.
5.2.4.1.1 Intake
For carcinogens, exposure is averaged over a lifetime (therefore, the averaging time is
70 years, the average lifetime). Daily intake is backcalculated from the target risk, oral
cancer slope factor, and exposure parameters such as the exposure frequency and duration.
For noncarcinogens, the daily intake is backcalculated by direct comparison to the
reference dose. The reference dose is the dose to which a person may be exposed daily over
a lifetime or significant portion of a lifetime without adverse effect
5.2.4.1.2 Water Concentration
"**** '
The water concentration is backcalculated from the daily intake of contaminant body
weight, and consumption rate of drinking water.
August 1995 5-55
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table 5-3. Exposure
— -
Media Concentrations for Water (mg/L)
Ingestion
Dermal
Bathing
Chemical
Acenaphthene
Acetone
Acetonitrile
Acetophenone
Acrolein
Acrylamide
AcrykMutrile
Aldrin
AUyl chloride
Aniline
Antimony
Arsenic
Barium
Benz(a)anthracene
Benzene
Benzidine
Benzo(a)pyrene
Benzo(6)fluoranthene
Benzyl alcohol
Benzyl chloride
Beryllium
Bis(2-chloroisopropyl) ether
Bis(2-chloremyl)ctner
Bis(2-ethylhexyl)phthalaie
Bromodichloromethane
Bromoform (tribromomethane)
Butanol
Butyl-4,6-dinitrophenol, 2-sec- (dinoseb)
Butylbenzylphthalate
CAS
83329
67641
75058
98862
107028
79061
107131
309002
107051
62533
7440360
7440382
7440393
56553
71432
92875
50328
205992
100516
100447
7440417
39638329
111444
117817
*•* 75274 .
75252
71363
88857
85687
Adult
resident
26400
46400
2e-01
46400
7e-01
2e-05
2e-04
56-06
' NA
le-02
le-02
6c-05
3C400
Se-05
3e-03
46-07
le-05
7e-05
16401
56-04
26-05
le-03
Se-05
6&-03
le-03
le-02
46400
4e-02
76400
Adult Child
resident resident
16400
.16403
76401
76401
16402
2e-02
2e-02
2e-07
NA
7e-01
66400
2e-02
16403
4e-06
2e-02
le-05
4e-07
2e-06
56402
4e-03
8c-03
8e-03
4e-03
8e-05
2e-02
2e-01
26402
le-01
36400
7e-01
66402
46401
46401
86401
5e-02
5C-02
Se-07
NA
26400
36400
5e-02
5e402
le-05
5e-02
3e-05
le-06
66-06
36402
le-02
2e-02
26-02
le-02
2e-04
Se-02
5e-01
16402
Se-02
26400
(continued)
August 1995
5-56
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-3 (continued)
Ingestioo
Dermal
Bathing
Chemical
Cadmium
Caibon disulfide
Caiton tetrachloride
Chlordane
Chloro-lJ-butadiene, 2- (chloroprene)
Chkxoaniline, p-
Chlorobenzene
CMorobenzilate
Chlorodibromomethane
Chloroform
ChlorophenoU 2-
Chromium VI
Chryscne
Copper
Cresoi. m-
Cresol, o-
Cresol.p-
Cumene
DDD
DDE
DDT
Di-fl-butyl phthalate
Di-n-octyl phthalate
Diallate
Dibenz(a./0anthracene
Dibromo-3-chloropiDpane, 1,2-
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,4-
Dichlorobenzidine, 33'-
Dichlorodifluoromethane
CAS
7440439
75150
56235
57749
126998
106478
108907
510156
124481
67663
95578
7440473
218019
7440504
108394
95487
106445
98828
72548
72559
50293
84742
. 117840
2303164
^53703 .
96128
95501
106467
91941
75718
Adult
resident
4e-02
4*400
76-04
7e-05
NA
le-01
It-Ql
3e44
le-03
le-02
2e-01
2e-01
3e-03
le+00
2e+00
26400
2e-01
16400
46-04
3e-04
3c-(M
46400
76-01
le-03
le-05
6e-05
36400
4e-03
2e-04
76400
Adutt
resident
16401
36401
36-03
5e-06
NA
26400
26400
3e-04
2e-02
2e-01
26400
76401
2e-04
56402
26401
26401
26400
16400
2e-05 -'
56-06
9*06
26400
36-03
86-04
26-07
9e-04
46400
, 56-03
5e-04
66401
Child
resident
76400
26401
8e-03
le-05
NA
16400
16400
86-04
46-02
5e-01
96-01
36401
4e-04
36402
16401
16401
16400
7e-01
56-05
le-05
2e-05
16400
le-03
2e-03
5e-07
2e-03
26400
le-02
le-03
36401
(continued)
August 1995
5-57
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-3 (continued)
... .
Ingestioo
Dermal
Bathing
Chemical
Dichloroethane, 1,1-
.Dichloroediane, 1.2-
Dichloroethylene, 1,1-
Dtchloroethylene, ds-\3.~
Dichloroe thy lene, from- 1,2-
Dichlorophenol, 2,4-
Dichlorophenoxyacetic acid, 2,4-
(2,4-D)
Dichloropropane, 1,2-
fxj_|,|(v__,_,_-_- i 1
uiciuoropropcnc, 1,3-
Dichloropropene, cw-13-
r\' L.I it-'
Jjiciuoropropene, irons- i,j-
Diddrin
Diethyl phthalale
Diethylstilbestrol
Dimethoate
Dimethyl phthalate
Dimethylbenz(a)anthracene, 7,12-
Dimethylbenzidine, 33'-
Dimethylphenol. 2,4-
Dimethyoxybenzidine, 33'-
Dinitrobenzene, 13-
Dinitrophenol. 2,4-
Dinitrotoluene, 2,4-
Dinitrotoluene, 2j6-
Dioxane, 1,4-
Diphenylamine
Disulfoton
Endosulfan
Endrin .
CAS
75343
107062
75354
156592
156605
120832
94757
78875
542756
10061015
10061026
60571
84662
56531
60515
131113
57976
119937
105679
119904
99650
51285
121142
,^606202
123911
122394
298044
115297
72208
Adult
resident
9e-04
9e-04
le-04
4e-01
7e-01
le-01
4e-01
le-03
5e-04
5c-04
5e-04
5e-06
3C401
2e-08
7e-03
4e+02
3e-06
9e-06
lt-01
6e-03
4e-03
7e-02
7e-02
4e-02
Se-03
9c-01
le-03
2e-01
le-02
Adutt
resident
le-02
2e-02
le-03
4e+00
66400
3e-01
36400
le-02
56-03
56-03
Se-03
2e-06
36402
46-09
26400
16404
56-08
76-05
56400
26-OL
le-01
36400
16400
8e-01
36400
16400
2e-03
6e-01
5e-03
Child
resident
36-02
6c-02
3e-03
26400
36400
2e-01
16400
4e-02
. le-02
le-02
le-02
4e-06
26402
le-08
Se-01
86403
le-07
2e-04
36400
6e-01
7e-02
16400
7e-01
46-01
96400
7e-01
le-03
3e-01
36-03
(continued)
August 1995
5-58
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table 5-3 (continued)
Ingestion
Dermal
Bathing
Chemical
Epichlorohydrin
Elhoxyethanol, 2-
Ethyl acetate
Ethyl ether
Elhyl methacrylate
Ethyl methanesulfonate
Ethylbenzene
Ethylene dibromide
Ethylene thiourea
Fluotanthene
Fluorene
Fonnaldehyde -
Formic acid
Furan
Heptachlor
Heptachlor epoxide
Hexachlofo-13-botadiene
Hexachlofobenzene
Hexachlorocyclohexane, a- (o-BHQ
Hexachlorocyclohexane, p- (p-BHQ
Hexachlorocyclohexane, y- (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
Hexachlorophene
Indeno(l23-c4) pyrene
Isobutyl alcohol
Isophorane
Kepone
Lead
Mercury
CAS
106898
110805
141786
60297
97632
62500
100414
106934
96457
206440
86737
50000
64186
110009
7644C"
1024573
87683
118741
319846
319857
58899
77474
67721
70304
193395
•**&*
-78831
78591
143500
7439921
7439976
Adult
resident
9e-03
16401
3e+01
7e+00
3e+00
3e-07
46400
le-06
le-W
16400
16400
76400
76401
4e-02
2e-05
96-06
le-03
5e-05
le-05
5e^)5
7e-05
36-01
6e-03
lc-02
2e-04
16401
9e-02
2e-06
1.5e-02
le-02
Adult
resident
16400
46403
36403
46402
76401
8e-05
66400
•• 36-05
le-01
2e-01
6e-01
16403
36404
8e-01
le-06
5e-06
3e-04
3e-06
3e-05
9e-05
le-04
3e-02
6e-03
le-04
4e-06
76402
26400
le-06
NA
46400
Child
resident
46400
26403
16403
26402
46401
2e-04
36400
76-05
3c-01
le-01
3e-01
76402
26404
5e-01
3e-06
le-05
9e-04
9e-06
7e-05
2e-04
4e-04
26-02
26-02
6e-05
le-05
4&402
56400
3e-06
NA
26400
' (continued)
August 1995
5-59
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table 5-3 (continued)
. • „ .
Ingestioo
. Dermal
Bathing
Chemical
MethaoykNutrUe
Methanol
Methoxychlor
Methyl bromide (bromomethane)
Methyl chloride (chloromethane)
Methyl ethyl ketone
Methyl feobutyl ketone
Methyl methacrylate
Methyl parathion
Methylcholanthrcne, 3-
Methylene bromide
Methylene chloride
Molybdenum
//-Nitrosodi-Ji-propylamine
Ar-Nitrosodiphenylamine
Af-Nitrosopipendine
AT-Nilrosopynolidine
Naphthalene
Naphthylamine
Nickel
Nitrobenzene
Nitropropane, 2-
Nitrosodi-n-butylaniine
Nitrosodiethylamine
Nitrosodimethylanune
Nitrosomethylethylaniine
Octamemylpyrophospnoiamide
Parathion
Pentachlorobenzene
Pentachloronitrobenzene (PCNB)
CAS
126987
67561
72435
74839
74873
78933
108101
80626
298000
56495
74953
75092
7439987
621647
86306
100754
930552
91203
91598
7440020
98953
79469
924163
55185
-** 62759
10595956
152169
56382
608935
82688
Adult
resident
4e-03
26401
2e-01
5e-02
NA
26401
26400
36400
96-03
le-05
4e-01
4e-02
2e-01
4e-05
6e-02
86-06
le-04
16400
NA
7e-01
26-02
NA
5e-05
2e-06
6c-06
le-05
7e-02
2e-01
36-02
le-03
Adult
resident
36-01
96403
7e-02
26400
NA
36403
76401
86401
66-02
86-08
16401
3e41
76401
4e-04
5e-02
2e-04
le-02
26400
NA
36402
36-01
NA
le-04
7e-05
9c-04
le-03
2e402
464)1
4e-03
2e-04
Child
resident
2e-01
56403
4e-02
16400
NA
26403
46401
46401
4e-02
2e-07
66400
9e-01.
36401
le-03
le-01
6e-04
4e-02
16400
NA
16402
le-01
NA
3e-04
2e-04
2e-03
3e-03
96401
2e-01
2e-03
5e-04
(continued)
August 1995
5-60
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table 5-3 (continued)
Chemical
Pentachlorophenol
Phenol
Phenyl mercuric acetate
Phenylenediamine, m-
Phorate
Polychlorinated biphenyls
Pronamide •
Pyrene
Pyridine
Safirole
Selenium
Strychnine
Stryene
TCDD, 23.7,8-
Tetnchlorobenzene, 1,2,4,5-
Tetrachloroethane. 1,1,1.2-
Tetxachloroethane, 1,1,2,2-
TetrachloroeUiylene
Tetrachlorophenol. 23,4,6-
Tetraethyldithiopyrophosphate
Thallium (I)
Toluene
Toluenediamine, 2,4-
Toluidine, o-
Toluidine, p-
Toxaphene
Trichloro-l^-trifluoroethane, 1,1,2-
Trichlorobenzene, 1^,4-
Trichloroethane, 1,1,1-
Trichloroethane, 1.1^2-
CAS
87865
108952
62384
108452
298022
1336363
23950585
129000
110861
94597
7782492
57249
100425
1746016
9594?--
630206
79345
127184
58902
3689245
7440280
108883
95807
95534
106490
11)01352
76131
120821
71556
79005
Ingestioa
Adult
resident
2e-03
26401
36-03
2e-01
7e-03
4e-05
36400
16400
4e-02
2e-03
2e-01
le-02
76400
2e-09
le-02
le-02
le-03
4e-01
16400
26-02
3e-03
76400
9e-05
le-03
le-03
3e-04
16403
4e-01
NA
Se-03
Dermal
Bathing
Adult Child
resident resident
le-04
56402
le-01
66401
lc-02
3e-07
76400
le-01
36400
2e-03
76401
6e-01
26401
le-11
3e-03
2e-02
4e-03
26400
6e-01
4e-02
16400
26401
4e-03 .
le-02
le-02
2e-05
36403
2e-0t
NA
26-02
4c-04
36402
66-02
36401
6e-03
7e-07
46400
76-02
16400
7e-03 .
364O1
3e-01
96400
3e-ll
2e-03
5e-02
le-02
16400
36-01
26-02
5e-01
le+Ol
le-02
36-02
3e-02
6e-05
2e403
le-01
NA
5e-02
(continued)
August 1995
5-61
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table 5-3 (continued)
Ingestion
Denaal
Bathing
Chemical
Trichloroethylene
Trichlorofluoromethane
Trichlorophenol, 2,4,5-
TifcNorophenol, 2,4,6-
Trichlorophenoxyacetic acid.
2AM245-T)
Trichlorophenoxypropionic acid, 2,4,5-
(silvex)
Trichloropropane, 1^3-
Trinitrobenzene, sym-
Tris (2.3-dibromopropyl) phosphate
Vanadium
Vinyl chloride
Xyienes (total)
Zinc
CAS
79016
75694
95954
88062
93765
93721
96184
99354
126727
7440622
75014
1330207
7440666
Adult
resident
3e-02
16401
4*fOO
3e-02
4c-01
3c-01
2c-01
2e-03
3c-05
3e-01
le-04
7e+01
16401
Adutt
resident
le-02
6e*01
36400
9c-03
16400
9e-01
26400
26-01
5e-04
16402
Se-04
16402
46403
Child
resident
36-02
36401
26400
3e-02
7e-01
56-01
16400
86-02
le-03
56401
2e-03
66401
26403
NA a Not applicable.
August 1995
5-62
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Intake via Drinking Water: Carcinogens — Adult Resident
•
Parameter
I
TR
AT
ED
EF
CSForal
/o TR*AT*365dtyr
ED »EF' CSForal
Default
Definition value*
Intake of contaminant (mg/kg/d)
Target individual risk level (unitiess) Iff*
Averaging time (yr) 70
Exposure duration (yr)
Exposure frequency (d/yr) 350
Oral cancer slope factor (mg/kg/d)'1
Central
tendency High-end
value value
Calculated
9 30
Chemical-specific
(5-37)
Refer to
5.2.9.1
52.9.1
52.9.1
52.9.1
52.9.6.3
• Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 199la).
August 1995
5-63
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Intake via Drinking Water:
/ -
Parameter Definition
I Intake of contaminant (mg/kg/d)
HQ Target hazard quotient (unitless)
RfD Oral reference dose (mg/kg/d)
•
Noncartinogens — Adult Resident
Uf\ • J?^^^
^&\^F t\f^J
Central
Default tendency High-end
value* value value
Calculated
1
Chemical-specific
(5-38,
Refer to
5.2.9.1
5.2.9.63
* Default values are standard default used in Agency risk assessments of policy used in this rule-making.
Source: RAGS Part B (U.S. EPA. 1991a).
••*»
August 1995
5-64
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Drinking Water Concentration— Adult Resident
I'BW
*"
(5-39)
Parameter
c*,
I
BW
CR
Definition
Dissolved water concentration (mg/L)
Intake of contaminant (mg/kg/d)
Body weight (kg)
Consumption rate of water (L/d)
* Default values are standard default used in Agency risk
Default
value*
70
assessments or p
Central
tendency High-end
value value
Calculated
From Equation 5-38
1.4 2.0
obey used in this rule?makii
Refer to
5.2,9.1
5.2.9.2
>g-
Source: RAGS Part B (U.S. EPA. 199 la).
*»*.
August 1995
5-65
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
5.2.4.2 Dermal Water Exposure
r ^
Starting with a target risk or hazard quotient, the concentration in contaminated surface
or groundwater to which humans are dermally exposed is backcalculated in three steps. First,
the daily average lifetime dose associated with the target risk or hazard quotient is
backcalculated. Two different algorithms are used to backcalculate daily average lifetime
dose, one for carcinogens and one for noncarcinogens. Second, the absorbed dose per
exposure event is then backcalculated from the average daily lifetime dose. Again, two
different algorithms are used to backcalculate absorbed dose per exposure event, one for
carcinogens and one for noncarcinogens. Finally, the concentration in water is backcalculated
from the absorbed dose per event Two different algorithms are used for organic and
inorganic constituents.
Dermal water exposures are calculated for both adults and children. Adults have a
longer exposure duration, but children have greater surface area to body weight ratio and,
hence, higher dermal exposure than adults. This ratio decreases consistently with age. For
bathing scenarios, a 1- to 6-year-old child resident was used to reflect the highest surface area
to body weight ratio. Dermal water exposures were calculated separately for children and
adults. .
For an adult resident exposed while bathing. Equations 5-40 and 5-41 show the
backcalculation of daily average dose for carcinogens and noncarcinogens, respectively.
Equations 5-42 and 5-43 show the backcalculation of absorbed dose per event for carcinogens
and noncarcinogens, respectively. Equations 5-44 and 5-45 show the backcalculation of water
concentration for organics under non-steady-state and steady-state conditions, respectively.
Equation 5-46 shows the backcalculation of water concentration for inorganics. Equations
5-47 through 5-53 present the same backcalculations for a child resident exposed while
bathing. These differ from the adult resident equations only in the inputs used.
5.2.4.2.1 Daily Average Lifetime Dase
j . . .
For carcinogens, daily average lifetime dose is a function of the target risk and the
cancer slope factor. Because appropriate cancer slope factors for dermal exposure are not
available, oral cancer slope factors are used, as recommended by EPA in the Dermal
document
August 1995
5-66
-------
5.0 EXPOSURE 5J Concentrations for Human Receptors
For noncarcinogens, daily average dose is a function of target hazard quotient and the
reference dose. Because appropriate reference doses for dermal exposure are not available,
oral reference doses are used, as recommended by EPA in the Dermal document
5.2.4.2.2 Absorbed Dose per Event
For carcinogens, exposure is averaged over a lifetime (therefore, the averaging time is
70 years, the average lifetime). Absorbed dose per event is backcalculated from the daily
average lifetime dose, body weight, skin surface area, and exposure parameters such as the
event frequency, exposure frequency, and duration.
For noncarcinogens, the absorbed dose per event is backcalculated from die daily
average dose, body weight, skin surface area, and the daily event frequency. Because the
daily average dose is based on a reference dose, which is the dose to which a person may be
exposed daily over a lifetime or significant portion of a lifetime without adverse effect,
exposure duration, frequency, and averaging time are not included in the calculation,
5.2.4.23 Water Concentration
Organics. A non-steady-state approach for estimating dermal absorbed dose from
water offers significant advantages over the traditional steady-state approach. First, it may
better reflect normal human exposure conditions, since the short contact times associated with
bathing generally mean that steady .state may not occur. Second, it accounts for the dose that
can occur after die actual exposure event due' .J absorption of contaminants stored in skin
lipids (U.S. EPA, 19924). Under the non-steady-state approach, the calculations differ
depending on whether the duration of the exposure event exceeds the time to reach steady
state so that part of the exposure occurs under non-steady-state conditions and part under
steady-state conditions, or whether the duration of the event is less than the time to reach
steady state so that the entire event occurs under non-steady-state conditions.
For both groundwater and surface water, the water concentration is dissolved water
concentration. Bathing exposures are assumed to occur from the same water supply as
drinking water, which is likely to be filtered. For groundwater, which is not necessarily
filtered, the water concentration is total water concentration.
The skin permeability factor, K^ reflects how easily constituents in water are absorbed
through the skin. The Bunge constant,!), characterizes the effect of die viable epidermis (the
second layer of the epidermis) on the cumulative mass of contaminant that enters the stratum
corneum (die outermost layer of the epidermis). The lag time, T, is a function of die
thickness of the stratum corneum and die molecular weight of die constituent
August 1995 5-67
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
Inorganics. As described above, a non-steady-state approach for estimating dermal
absorbed dose from water offers significant advantages over the traditional steady-state
approach. However, the non-steady-state approach was developed for application to organics,
which exhibit octanol-water partitioning. Thus, it is not applicable to inorganics. Therefore,
a steady-state approach is applied to inorganics (U.S. EPA, 1992d).
August 1995 5-68
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Daily Average Lifetime Dose via Dermal Contact with Water:
Carcinogens — Bathing, Adult
PAP = TR
CSForal
Central
Default tendency High-end
Parameter Definition value* value value
(5-40)
Refer to
DAD Daily average lifetime dose (mg/kg/d) Calculated
TR Target individual lifetime risk (unitless) Iff*
CSForal Oral cancer slope factor (mg/kg/d) Chemical-specific
5.2.9.1
5.2.9.6J
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d). . .
August 1995
5-69
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Daily Average Dose via Dermal Contact with Water:
Noncarcinogens — Bathing, Adult
DAD * HQ'RfD
(5-41)
Parameter
DAD
HQ
RfD
Definition
Daily average dose (mg/kg/d)
Target hazard quotient (unitless)
Reference dose (mg/kg/d)
Default
value*
/
1
Central
tendency High-end
value value
Calculated
Chemical-specific
Refer to
5.2.9.1
5.2.9.63
• Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (US. EPA. 1992d). .
August 1995
5-70
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
1
Parameter
D*_
DAD
AT
BW
EV
EF
ED
A
Absorbed Dose per Event: Carcinogens— Bathing, Adult
pA m DAD*AT'365d/yr*BW
«•"•". EV'EF'ED'A
t
Central
Default tendency High-end
Definition value* value value
Dose absorbed per unit area per event Calculated
(mg/cm2/event)
Daily average lifetime dose (mg/kg/d) From Equation 5-40
Averaging time (yr) 70
Body weight (kg) 70
Event frequency (events/d) 1
Exposure frequency (d/yr) 350
Exposure duration (yr) 9 30 .
Exposed skin surface area (cm2)6 . 20,000
(5-42)
Refer to
52.9.1
52.9.1
52.9.4
52.9.1
52.9.1
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Skin surface area is not varied due to its relationship to body weight, for which a default value is used.
Source: Dermal document (US. EPA, 1992d).
August 1995
5-71
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Absorbed Dose per Event: Noncarcinogens — Bathing, Adult
Parameter
DA-
DAD
BW
EV
A
""event —
Definition
Dose absorbed per unit area per event
(mg/cm2/event)
Daily average dose (mg/kg/d)
Body weight (kg)
Event frequency (events/d)
Exposed skin surface area (cm2)b
EV*A
Central
Default tendency High-end
value* value value Refer to
Calculated
From Equation 5-41
70 52.9.1
1 52.9.4
20,000 52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Skin surface area is not varied due to its relationship to body weight, for which a default value is used.
Source: Dermal document (US. EPA, 1992d).
August 1995
5-72
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Water Concentration: Organics—Bathing, Adult
'water.
ar"
event
K
(5-44)
'event*'*
+2t
l*B
(5-45)
Parameter
Cw^
DA_
V
vVCOt
T
t*
B
* Default values
Default
Definition . value*
Water concentration (mg/L)
Dose absorbed per unit area per event
(mg/cm2)
Skin permeability constant in water (cm/h)
Duration of event (h)
Lag time (h)
Time to steady-state skin flux (chemical-
specific) (h)
Bunge constant (unitless)
are standard default used in Agency risk assessments or p
Central
tendency High^end
value value
Calculated
From Equations 5-42
and 5-43
Chemical-specific
0.17 025
Chemical-specific
Chemical-specific
Chemical-specific
olicy used in this rule-makii
Refer to
52.9.62
52.9.4
52.9.62
52.9.62
52.9.62
*g-
Source: Dermal document (U.S. EPA, I992cfj?»
August 1995
5-73
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
1
. Water Concentration:
C - -
Parameter Definition
Cwa,, Concentration in water (mg/L)
Inorganics — Bathing, Adult
M_.,OW/L
Kf'teve*
Central
Default tendency High-end
value* value . value
Calculated
(5-46)
Refer to
DAevo* Dose absorbed per unit area per event From Equations 5-42
(mg/cm2/event) and 5-43
.Kp* Skin permeability constant in water
tevoj. Duration of event (h)
(cm/h) Chemical-specific
0.17 025
52.9.62
52.9.4
• Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA. 1992d).
August 1995
5-74
-------
5.0 EXPOSURE
S3, Concentrations for Human Receptors
•
Daily Average Lifetime Dose via Dermal Contact with Water:
Carcinogens — Bathing, Child
PAP = TR
CSForal
Default
Parameter Definition . value*
DAD Daily average lifetime dose (mg/kg/d)
TR Target individual lifetime risk (unitless) 10"*
CSForal Oral cancer slope factor (mg/kg/d) .
(5-47)
Central
tendency High-end
value value . Refer to
Calculate!
5.2.9.1
Chemical-specific 52.9.63
Source: Dermal document (US. EPA, 1992d).
August 1995
5-75
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Parameter
DAD
HQ
RfD
Daily Average Dose via Dermal Contact
Noncartinogens— Bathing, Chi
DAD = HQ »RfD
\
Default
Definition value*
Daily average dose (mg/kg/d)
Target hazard quotient (unitless) 1
Reference dose (mg/kg/d)
with Water:
Id
(5-48)
Central
tendency High-end
value • value Refer to
Calculated
52.9.1
Chemical-specific 52.9.6.3
' Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-76
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
1
Parameter
D*^
DAD
AT
BW
EV
EF
ED
A
Absorbed Dose per Event:
™ ,DAD
Definition
Dose absorbed per unit 'area per event
(mg/cm2/event)
Daily average lifetime dose (mg/kg/d)
Averaging time (yr)
Body weight (kg)
Event frequency (events/d)
Exposure frequency (d/yr)
Exposure duration (yr)
Exposed skin surface area (cm2)b
'
Carcinogens— Bathing Child
.AT.365dlyr.BW
W.EF.ED.A
Central
Default tendency High-end
value* value ' value
Calculated
From Equation 5-47
70
15
1
350
6
6,900
' '
(5-49)
Refer to
52.9.1
52.9.1
52.9.4
52.9.1
5.2.9.1
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Skin surface area is not varied due to its relationship to body .weight, for which a default value is used.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-77
-------
5.0 EXPOSURE
5 J Concentrations for Human Receptors
Absorbed Dose per Event: Noncarcinogens — Bathing, Child
(5-50)
•
Parameter
DA™
DAD
BW
EV
A
Definitioa
Dose absorbed per unit area per event
(mg/cm2/event)
Daily average dose (mg/kg/d)
Body weight (kg)
Event frequency (events/d)
Exposed skin surface area (cm2)6
EV*A
Central
Default tendency High-end
value* value value
Calculated .
From Equation 5-48
15
1
6.900
Refer to
52.9.1
52.9.4
52.9.4
• Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Skin surface area is not varied due to its relationship to body .weight, for which a default value is used.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-78
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Water Concentration: Organics—Bathing, Child
for Wnt <
cm3/!
'water
6U
event
(5-51)
'event * «*
(5-52)
Parameter
c**,
DA_
V
tevo.
t
'.t* .
B
' Definition
Water concentration (mg/L)
Dose absorbed per unit area per event
(mg/cm2)
Skin permeability constant in water (cm/h)
Duration of event (h)
Lag time (h)
Time to steady-state skin flux (chemical-
specific) (h)
Bunge constant (unitless)
Central
Default tendency High-end
value* value value
Calculated
From Equations 5-49
and 5-50
Chemical-specific
0.17 033
Chemical-specific
Chemical-specific
Chemical-specific
Refer to
52.9.62
5.2.9.4
52.9.62
52.9.62
52.9.62
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (US. EPA,
August 1995
5-79
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Water Concentration: Inorganics— Bathing, Child
cm3/L
'water
(5-53)
Parameter
Definition
Central
Default tendency High-end
value* value value Refer to
Cw^
DA-
V
U*
Concentration in water (mg/L) •
Dose absorbed per unit area per event
(mg/cm2/event)
Skin permeability constant in water (cm/h)
Duration of event (h)
Calculated
From Equations 5-40
and 5-50
Chemical-specific
1 0.17 0.33
5.2.9.6.2
52.9.4
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dermal document (U.S. EPA, 1992d).
August 1995
5-80
-------
5.0 EXPOSURE 5J Concentrations for Human Receptors
5.2.5 Plant Concentrations
t-t
Ing Ing
==> or ==>
Veg Root
Plant concentrations are backcalculated for aboveground firuits and vegetables and root
vegetables based on ingestipn of these plants by a home gardener or subsistence farmer.
Table 5-4 summarizes the backcalculated plant concentrations for aboveground fruits and
vegetables and root vegetables by chemical and receptor.
Starting with a target risk or hazard quotient, the concentration in contaminated plants
ingested by humans is backcalculated in two steps. First, intake associated with the target
risk or hazard quotient is backcalculated. Two different algorithms are used to backcalculate
intake, one for carcinogens and one for noncarcinogens. The plant concentration is then
backcalculated from the intake. Intake refers to the quantity of contaminant ingested daily,
normalized by body weight
For the home gardener, Equations 5-54 and 5-55 show the backcalculations from intake
for carcinogens and noncarcinogens, respectively. Equation 5-56 shows the backcalculation
of plant concentration from intake for the home gardener. Equations 5-57 through 5-59 are
the analogous equations for the subsistence fc±ner, these differ from those for the home
gardener only in the inputs used.
5.2.5.1 Intake
For carcinogens, exposure is averaged over a lifetime (therefore, the averaging time is
70 years, the average lifetime). Daily intake is backcalculated from the target risk, oral
cancer slope factor, and exposure parameters such as the exposure frequency and duration.
For noncarcinogens, the daily intake is backcalculated by direct comparison to the
reference dose. The reference dose is the dose to which a person may be exposed daily over
a lifetime, or significant portion of a lifetime without adverse effect
5233, Plant Concentration "*'.
The concentration in a particular plant group (e.g., aboveground fruits and vegetables or
root vegetables) is backcalculated from the daily intake of contaminant and depends on body
weight, consumption rate of the plant group, and the fraction of the plant group consumed
that is contaminated. For example, a home gardener may ingest some contaminated vege-
tables from his garden and some uncontaminated vegetables from a grocery store; the fraction
contaminated would reflect that not all of the vegetables consumed by this person are
contaminated.
August 1995 " 5-81
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-4. Exposure Media Concentrations for Plants (rag/kg)
Ingestion
Aboveground
fruits and vegetables
Chemical
Aceriaphthene
Acetone
Acetohitrile '
Acetophenone
Acrotein
Acrylamide
Acrylonitrile
Aldrin
AJlyl chloride
Aniline
Antimony
Arsenic
Barium
Benz(a)anthracene
Benzene
Benzidine
Benzo(a)pyrene
Benzo(l>)fluoranthene
Benzyl alcohol
Benzyl chloride .
Beryllium
Bis(2-chloroisopropyl) ether
Bis(2«hk)rethyl)etner
Bis(2-ethylhexyl)phthalaie
Bromodichlorofnethane
Butanol
Butyl-4^S-dinitrophenol 2-sec-
(dinoseb)
Butylbenzylphthalate
*
CAS
83329
67641
75058
98862
107028
79061
107131
309002
107051
62533
7440360
7440382
7440393
56553
71432
92875
50328
205992
100516
100447
7440417
39638329
111444
117817
75274
gtsj . .
75252*
71363
88857
85687
•
Home
gardener
6&f02
96402
66401
9e402
26402
5e-03
4e-02
le-03
NA
46400
46400
le-02
66402
2e-02
/e-01
9e-05
3e-03
2e-02
3e403
le-01
5e-03
3e-01
2e-02
26400
36-01
36400
96402
96400
26403
Subsistence
farmer
26402
4e402
26401
46402
86401
26-03
le-02
4e-04
NA
16400
26400
5e-03
36402
7e-03
2e-01
'3e-05
le-03
6e-03
16403
4e-02
2e-03
le-01
7e-03
5e-01
le-01
9e-01
46402
46400
86402
Root vegetables
Home
gardener
36403
46403
36402
4e403.
96402
2e-02
2e-01
6e-03
NA
26401
26401
7e-02
36403
9e-02
36400
4e-04
le-02
8c-02
16404
6e-01
2e-02
16400
9e-02
76400
26400
16401
46403
46401
96403
Subsistence
farmer
16403
26403
16402
2e403
46402
8e-03
6e-02
2e-03
NA
66400
86400
2e-02
16403
3c-02
16400
le-04
5e:03
3e-02
66403
2e-01
8e-03
5e-01
3e-02
26400
5e-01
4e400
2&403
26401
46403
(continued)
August 1995
5-82
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-4 (continued)
Ingestion
Aboveground
fruits and vegetables
Chemical
Cadmium
Caibon disulfide
Caibon tetrachloride
Chlordane
Chloro-U-butadiene, 2-
(chloroprene)
Chloroaniline, p-
Chloiobenzene
Chlorobenzilate
ChkxodfcromomeUiane
Chlorofonn
Chlorophenol, 2-
Chromium VI
Chrysene
Copper
Cresol, m-
Cresol, o-
CresoLp-
Cumene
DDD
DDE
DDT
Di-n-butyl phthafeue
Di-n-octyl phthalate
DiaUate
Dibenz(a,/i)anthracene
rtikwMWfl ^ rL|.in_.lrLrmfiginm» 1 ^
DiocoiTKhj-cnioropropane, 1,2-
TMjhl.TLJiriK«n ••>•!-«. 1 *>
Dichlorobenzene, 1.4-
Dichlorobenzidine, 33'*
CAS
7440439
75150
56235
57749
126998
106478
108907
510156
124481
67663
95578
7440473
218019
7440504
108394
95487
106445
98828
72548
72559
50293
84742
117840
2303164
5370T1
96128
95501
106467
91941
Home
gardener
96400
96402
2e-01
26-02
NA
46401
26402
Se-02
3*01
46400
56401
56401
76-01
1-402
56402
56402
56401
46402
9e-02
6e-02
6*02
96402
26402
4e-0l
36-03
2e-02
86402
9e-01
5e-02
Subsistence
farmer
46400
46402
66-02
66-03
NA
26401
86401
36-02
96-02
16400
26401
26401
26-01
26403
•26402
26402
26401
26402
36-02
2e-02
26-02
46402
86401
le-01
96-04
5e-03
46402
36-01
26-02
Root vegetables
Home
gardener
46401
46403
8e-01
86-02
NA
26402
96402
4e-01
16400
26401
26402
26402
36400
26402
26403
26403
26402
26403
46-01 .
36-01
36-01
46403
96402
26400
le-02
76-02
46403
46400
2e-01
Subsistence
fanner
26401
26403
3e-01
36-02
NA
86401
46402
le-01
4e-01
66400
16402
16402
16400
76402
16403
16403
16402
86402
le-01
le-01
le-01
26403
46402
6e-01
4e-03
2e-02
26403
16400
8e-02
(continued)
August 1995
5-83
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table
5-4 (continued)
•;....'•-.. Ingestion
Aboveground
fruits and vegetables
Chcmkal
Dichlorodifluoromethane
DichJoroethane, 1,1-
Dichloroethane, 1,2-
Dichloroethylene, 1,1-
Dichloroethylene, cis-12-
Dichloroethylene, trans-12-
Dichlorophenol, 2,4-
Dichlorophenoxyacetic acid, 2,4-
(2,4-D)
Dtchloropropane, 1,2-
Dichloropropcne. 13-
DichJpropfopene, cfa-13-
W^.« L« ' « K»
Diciuoropropene, trans-\3-
Dieldrin
Diethyl phthalate
Diethylstilbestrol
Dimethoate
Dimethyl phthalate
Dimethylbenz(
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table
5-4 (continued)
Ingestion
Aboveground
fruits and vegetables
Chemical
Endrin
Epichlorohydrin
Ethoxyethanol 2-
Elhylacetaie
Ethyl ether
Ethyl methacrylate
Ethyl methariesulfonate
Ethylbenzene
Ethylene dibromide
Ethylene thiourea
Fluoranthene
Fluorene
Fonnaldehyde
Fonnicacid
Funui
Heptachlor
Heptachlor epoxide
Hexachtoro-U-butadiene
Hexachlorobenzene
Hexachlorocyclohexane. o>
(a-BHQ
Hexachlorocyclohexane, P-
0-BHQ
Hexachlorocyclohexane. Y-
(lindane)
Hexachlarocyclopentadiene
Hexachloroethane
Hexachlorophene
Indeno(123-c^) pyrene
Isobutyl alcohol
CAS
72208
106898
110805
141786
60297
97632
62500
100414
106934
96457
206440
86737
50000
64186
110009
76448
1024573
87683
118741
319846
319857
58899
77474,
67721'
70304
193395
78831
Home
gardener
36400
26400
46403
86403
2e403
86402
7e-05
96402
3e-04
4e-02
46402
46402
26403
26404
96400
5e-03
2e-03
36-01
le-02
3e-03
le-02
26-02
66401
26400
36400
56-02
36403
Subsistence
fanner
16400
7e-01
26403
46403
86402
46402
26-05
46402
Se-05
le-02
26402
26402
86402
86403
46400
26-03
86-04
96-02
5e-03
le-03
4e-03
6e-03
36401
5e-01
16400
26-02
16403
Root vegetables
Home
gardener
16401
16401
26404
46404
96403
46403
3e-04
46403
le-03
26-01
26403
26403
96403
96404
46401
26-02
le-02
16400
66-02
2e-02
66-02
Se-02
36402
76400
16401
36-01
16404
Subsistence
farmer
66400
36400
86403
26404
46403
26403
le-04
26403
46-04
66-02
86402
86402
46403
46404
26401
Se-03
4e-03
4e-01
2e-02
56-03
2e-02
3e-02
16402
26400
66400
Se-02
66403
(continued)
August 1995
5-85
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table
5-4 (continued)
Ingestion
Aboveground
. fruits and vegetables
Chemical
Isophorone
Kepone
Mercucy
Methacrylonilrile
Methanol
Methoxychkr
Methyl bromide (bromomethane)
Methyl chloride (chloromethane)
Methyl ethyl ketone
Methyl isobutyl ketone
Methyl methacrylate
Methyl parathion
Methylcholanthrene. 3-
Methytene bromide
Methylene chloride
Molybdenum
N-Nitrosodi-n-propylamine
N-Nitrosodiphenylamine
A/-Nitrosopiperidine
yV-Nitrosopynolidine
Naphthalene
Naphthylamine
Nickel
Nitrobenzene
Nitropropane, 2-
Nitrosodi-n-butylamine
Nitrosodiethylamine
Nitjosodimethylamine
Nitrosomethylethylamine
Octamethylpyrophosphoramidb
CAS
78591
143500
7439976
126987
67561
72435
74839
74873
78933
108101
80626
298000
56495
74953
75092
7439987
621647
86306
100754
930552
91203
91598
7440020
98953
7946SU
924163*
55185
62759
10595956
152169
Home
gardener
26401
5e-04
36400
9e-01
56403
56401
16401
NA
66403
56402
76402
26400
8e-04
96401
36400
56401
3e-03 <
46400
66-04
le-02
46402
NA .
26402
56400
NA
4e-03
le-04
4e-04
le-03
26401
Subsistence
fanner
86400
2e-04
16400
4e-01
26403
26401
66400
NA
26403
26402
36402
16400
3e-04
46401
16400
26401
le-03
16400
2e-04
36-03
26402
NA
86401
26400
NA
le-03
5e-05
le-04
3e-04
86400
Root vegetables
Home
gardener
16402
2e-03
16401
46400
26404
26402
66401
NA
36404
2e403
36403
16401
4e-03
46402
16401
26402
le-02
26401
3e-03
5e-02
26403
NA
96402
26401
NA
2e-02
7e-04
26-03
5e<03
96401
Subsistence
farmer
46401
7e-04
66400
26400
16404
16402
36401
NA
16404
16403
26403
56400
le-03
26402
56400
16402
5e-03
76400
9e-04
2e-02
86402
NA
46402
16401
NA
6e-03
2e-04
7e-04
2e-03
46401
• (continued)
August 1995
5-86
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
\
Table 5-4 (continued)
Ingestioo
Aboveground
fruits and vegetables
Chemical
Parathion
Pentachlorobenzene
Pentachldronitrobenzene (PCNB)
Pentachlorophenol
Phenol
Phenyl mercuric acetate
Phenylenediamine, m-
Phorate
Polychlorinated biphenyls
Pronamide
Pyrene
Pyridine
Safrole
Selenium
Strychnine
Sfryene
TCDD, 2.3.7.8-
Tetrachlorobenzene, 1,2,4,5-
Tetrachloroethane, 1,14,2-
Tetrachloroethane, 1,1,2,2-
Tetrachloroethylene
Tetrachlorophenol, 2,3,4,6-
Tetraethyldithiopyrophosphate
Thallium (I)
Toluene
Toluenediamine, 2.4-
Toluidine, o-
Toluidine. p-
Toxaphene
CAS
56382
608935
82688
87865
108952 .
62384
108452.
298022
1336363
23950585
129000
110861
94597
7782492
57249
100425
1746016
95943
630206
79345
127184
58902
3689245
7440280
lo&yg
9580T
95534
106490
. 8001352
Home
gardener
66401
76400
Se-02
2e41
66403
7e-01
66401
2e+00
3«-03
7e+02
3C402
96400
le-01
Se+01
3e+00
2e+03
le-07
36400
8e-01
le-01
96401
36402
56400
7e-01
2e403
7eX)3
96-02
le-01
2e-02
Subsistence
fanner
26401
36400
36-02
6e-02
26403
3e-0l
26401
8e-01
9e-04
36402
16402
46400
4e-02
26401
ieibo
86402
5e-08
16400
3e-01
46-02
46401
16402
26400
36-01
86402
2e-03
3e-02
4e-02
7e-03
Root vegetables
Home
gardener
36402
36401
4c-01
8e-01
36404
36400
36402
96400
le-02
3e$03
16403
46401
6e-01
26402
16401
96403
6e-07
16401
4e400
56-01
46402
16403
26401
36400
96403
3e-02
4e-01
5e-01
9e-02
Subsistence
farmer
16402
26401
le-01
3e-01
16404
26400
16402
46400
4e-03
16403
66402
26401
2e-01
16402
66400
4e403
2e-07
66400
16400
26-01
26402
66402
16401
26400
46403
le-02
le-01
2e-01
3e-02
(continued)
August 1995
5-87.
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table
5-4 (continued)
Ingestioo ;
Aboveground
fruits and vegetables
Chemical
Trichloro- 1 ,2.2-trifluoroethane,
1,U-
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethane, 1,1,2-
Trichlorocthylenc
Trichlorofluoromethane
Trichlorophenol, 2,4,5-
Trichlorophenol, 2,4,6-
Trichlorophenoxyacetic acid,
2AM2AS-T) ':'
Trichlorophenoxypropionic acid,
2,4,5- (silvex)
Trichloropropane, 1,2.3-
Trinitrobenzene, sym-
Tris (23-dibromopropyl) phosphate
Vanadium
Vinyl chloride
Xylenes (total)
Zinc ,
CAS
76131
120821
71556
79005
79016
75694
95954
88062
93765
93721
96184
99354
126727
7440622
75014
1330207
7440666
Home
gardener
36405
9e+01
NA
4e-01
26400
3e+03
9e+02
2e-fOO
96401
7e>01
6e-f01
5e-01
26-03
66401
le-02
26404
36403
Subsistence
fanner
16405
46401
NA
16-01
7e-01
16403
46402
7e-0l
46401
36401
26401
2e-01
7e-04
36401
4e-03
. 86403
16403
Root vegetables
Home
gardener
16406
46402
NA
26400
96400
16404
46403
96400
46402
36402
36402
26400
le-02
36402
56-02
96404
16404
Subsistence
farmer
66405
2C402
NA
6e-01
36400
66403
26403
36400
26402
26402
16402
16400
36-03
16402
26-02
46404
66403
NA = Not applicable.
August 1995
5-88
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Intake via Plant Ingestion: Carcinogens — Home Gardener
•
-. ,.re.AT.365d
Vyr'l&pg/mg (5.54)
ED* EF •CSForal
Parameter
I
TR
AT
ED
EF
CSForal
Definition
Intake of contaminant (ug/kg/d)
Target individual risk level .(unitless)
Averaging time (yr)
Exposure duration (yr)
Exposure frequency (d/yr)
Oral cancer slope factor (mg/kg/d)"1
Central
Default tendency High-end
value* value value Refer to
Calculated
10-* 5.2.9,1
70 5.2.9.1
9 30 5.2.9.1
175 5.2.9.1
Chemical-specific 52.9.63
4 Default values are standard default.used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 1991a).
August 1995
5-89
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
Intake via Plant Ingestion: Noncarcinogens—Home Gardener
= HQ*RjD'lQ3pg/mg
(5-55)
Parameter
I
HQ
RfD
Definition
Intake of contaminant (ug/kg/d)
Target hazard quotient (unitless)
Oral reference dose (mg/kg/d)
Default
value*
1
Central
tendency High-end
value value
Calculated
Chemical-specific
Refer to
5.2.9.1
52.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 1991a).
August 1995
5-90
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Plant Concentration — Home Gardener
Parameter
Cpta*
I
BW
F
CR
c
Definition
Concentration in plant (ug/g)*
Intake of contaminant (ug/kg/d)
Body weight (kg)
Fraction grown in contaminated
soil (unitless)
Consumption rate of plant
(g/d)b
= l*BW
Central
Default tendency High-end
value*. value value
Calculated
From Equations 5-54 and
5-55
70
0.25 0.40
19.7
(aboveground)
28 (root)
(5-56)
Refer to
5.2.9.1
5.2.9.2
5.2.92
* Default values are standard default used in Agency risk assessments or policy used in mis rule-making.
b Aboveground fruit and vegetable concentration and consumption rate are in dry weight. Root vegetable
concentration and consumption rate are in whole weight
Source: RAGS Pah B (U.S. EPA. 199 la).
August 1995
5-91
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
. Intake via Plant Ingestion: Carcinogens— Subsistence Farmer
- , . 7*Mr.365,
d/yr*W3}ig/mg (5_51}
ED'EF'CSForal
Parameter
I
TR
AT
ED
EF
CSForal
Definition
Intake of contaminant (ug/kg/d)
Target individual risk level (unitless)
Averaging time (yr)
Exposure duration (yr)
Exposure frequency (d/yr)
Oral cancer slope factor (mg/kg/d)'1
Central
Default tendency High-end
value* value value Refer to
Calculated
ID"6 . 5.2.9.1
70 5.2.9.1
20 40 5.2.9.1
350 5.2.9.1
Chemical-specific 5.2.9.63
" Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 1991a).
August 1995
5-92
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
Intake.via Plant Ingestion: Noncarcinogens—Subsistence Farmer
Central
Default tendency High-end
Parameter Definition value* value value Refer to
I Intake of contaminant (ug/kg/d) Calculated
HQ Target hazard quotient (unitless) 1 52.9.1
RfD Oral reference dose (mg/kg/d) Chemical-specific 5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA. 1991a).
August 1995 5-93
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
_ - Plant Concentration — Subsistence Farmer
Cpl
Parameter Definition
Cptaa Concentration in plant
(ug/g)*
I Intake of contaminant
(ug/kg/d)
BW Body weight (kg)
F . Fraction grown in
contaminated soil (unitless)
CR Consumption rate of plant
(g/d)b
-•'£?* <559)
Central tendency
Default value High-end
value" . ' value Refer to
Calculated
From Equations 5-57 and 5-58
70 5.2.9.1
0.4 0.9 5.2.9.2
19.7 (aboveground) . 5.2.9.2
28 (root)
• Default values are standard default used in Agency risk assessments or policy used in this rule-making.
b Aboveground fruit and vegetable concentration and consumption rate are in dry weight. Root vegetable
concentration and consumption rate are in whole w..0hL
Source: RAGS Part B (U.S. EPA, 199la).
August 1995
5-94
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
5.2.6 Beef and Milk Concentrations
Concentrations in beef and milk are backcalculated based on ingestion of these animal
products by a subsistence farmer. Table 5-5 summarizes the backcalculated animal product
concentrations for beef and milk by chemical.
Starting with a target risk or hazard quotient, the concentration in contaminated animal
products ingested by humans is backcalculated in two steps. First, intake associated with the
target risk or hazard quotient is backcalculated. Two different algorithms are used to
backcalculate intake, one for carcinogens and one for nbncarcinogens. The concentration in
animal products is then backcalculated from the intake. Intake refers to the quantity of
contaminant ingested daily, normalized by body weight.
Equations 5-60 and 5-61 show the backcalculations for intake for carcinogens and
noncarcinogens, respectively. Equations 5-62 and 5-63 show the backcalculations for
concentration in animal tissue from intake for most chemicals and for dioxin-like compounds,
respectively.
5.2.6.1 Intake
For carcinogens, exposure is averaged over a lifetime (therefore, the averaging time is
70 years, the average lifetime). Daily intake is backcalculated from the target risk, oral
cancer slope factor, and exposure parameters such as the exposure frequency and duration.
For noncarcinogens, the daily intake is backcalculated by direct comparison to the
reference dose. The reference dose is the dose to which a person may be exposed daily over
a lifetime or significant portion of a lifetime without adverse effect
5.2.6.2 Beef or Milk Concentration
• •**».
The concentration in a particular*animal product (e.g., beef or milk) is backcalculated
from the daily intake of contaminant and depends on body weight, consumption rate of the.
animal product, and the fraction of the animal product consumed that is contaminated. For
example, a farmer may ingest some contaminated beef from his farm and some uncontam-
inated beef from a grocery store; the fraction contaminated would reflect that not all of this
person's consumption of beef is contaminated. The equations are modified slightly for
dioxins and PCBs, which tend to bioaccumulate in lipid material (i.e., fat) (Dioxin document,
U.S. EPA, 1992c).
August 1995 5-95
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-5. Exposure Media Concentrations for Animal Products
(mg/kg)
Ingestioo
Chemkal
Acenaphthene
Acetone
Acetonitrile
Acetophenone
Acrotein
Acrylainide
Aciylonitrile
Aldrin
Allyl chloride
Aniline
Antimony
Arsenic
Barium
Benz(a)anthracene
Benzene
Benzidine
Benzo(a)pyrene
Benzo(6)fluoranthene
Benzyl alcohol
Benzyl chloride
Beryllium
Bis(2-chloroisopropyl) ether
Bis(2-chloreihyl)ether
Bis(2-ethylhexyl)phthalate
Bromodkhloromethane
Bromoform (tribromomethane)
Butanol
Butyl-4,6-dinilrophenol, 2-sec- (dinoseb)
Butylbenzylphthalate
Cadmium
CAS
83329
67641
75058
98862
107028
79061
107131
309002
107051
62533
7440360
7440382
7440393
56553
?1432
92875
50328
205992
100516
100447
7440417
39638329
111444 ,
117817
75274
75252
71363
88857
85687
7440439
Beef
Subsistence
farmer
9e+01
le+02
9e+00
16402
3e+01
6e-04
5e-03
le-04
NA
4e-01
6e-01
2e-03
le+02
26-03
96-02
le-05
36-04
26-03
4e+02
le-02
6e-04
46-02
2e-03
26-01
46-02
36-01.
le+02
le+00
3e+02
le+00
Milk
Subsistence
farmer
3e+01
4e+01
3e+00
4e+01
9e+00
2e-04
le-03
5e-05
NA
le-01
2e-01
5e-04
3e+01
7e-04
3e-02
3e-06
le-04
7e-04
le+02
5e-03
2e-04
le-02
7e-04
6e-02
le-02
le-01
4e+01
4e-01
9e+01
4e-01
(continued)
August 1995
5-96
-------
5.0 EXPOSURE
S3 Concentrations for Human Receptors
, -- :
Chemical
Carbon disulfide
CartxMt tetrachloride
Chlordane
Chkxo-13-butadiene, 2- (chloroprene)
Chkxoaniline, p-
Chlorobenzene
Chlorobenzilate
Chlorodibromomethane
Chlorofonn
Chlorophenol, 2-
Chromium VI
Chryscne
Copper
Cresol. m-
Cresol, o-
Cresol, p-
Cumene
ODD
DDE
DDT
Di-fi-butyl phthalate
Di-n-octyl phthalate
Diallate
Dibenz(a,/i)anthracene
Dibromo-3-chloropropane, 1,2-
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,4-
Dichlorobenzidine, 33'-
Dichlorodifluoromethane
Dichloroethane, 1,1-
Table 5-5 (continued)
CAS
75150
56235
57749
126998
106478
108907
510156
124481
67663
95578
7440473
218019
. . 7440504
108394
*5487
106445
98828
72548
72559
50293
84742
117840
2303164
53703
» %1M
95501
106467
91941
75718
75343
Ingestioo
Beef
Subsistence
farmer
16402
2e-02
2e-03
NA
6e-fOO
36401
9e-03
36-02
4e-01
76400
76400
8e-02
56401
76401
76401
76400
66401
le-02
7e-03
7e-03
16402
36401
46-02
3e-04
2e-03
16402
le-01
6e-03
36402
36-02
Milk
Subsistence
farmer
46401
6e-03
6e-04
NA
26400
96400
36-03
9e-03
le-01
26400
26400
2e-02
26401
26401
26401
26400
26401
3e-03
2e-03
2e-03
4e+01
9C400
le-02
le-04
6e-04
46401
3e-02
2e-03
96401
9e-03
(continued)
August 1995
5-97
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
Chemical
Dichloroethanc, 1,2- .
Dichloroethylene, 1,1-
Dichloroethylene, cis-12-
Dichloroethylene, trans-13.-
Dichlorophenol, 2,4-
Dichlorophenoxyacetic acid. 2,4- (2.4-D)
Dichloropropane, 1,2-
Dichloropropene, 13-
Dtchloropropene, ci>13-
Dichloropropene, trans-1.3-
Dieldrin
Diethyl phthalate
Diethylstilbestrol
Dimethoate
Dimethyl phthalate
Dimethylbenz(a)anthracene, 7,12-
Dimethylbenzidine, 33'-
Dimethylphenol, 2,4-
Dimethyoxybenzidine, 33'-
Dinitrobenzene, 1,3-
Dinitrophenol, 2,4-
Dinitrotoluene, 2,4-
Dinitrotoiuene, 2,6-
Dioxane, 1,4-
Diphenylamine
Disulfoton
Endosulfan
Endrin
Epichlorohydrin
Ethoxyethanol, 2-
Table 5-5 (continued)
-
CAS
107062
75354
156592
156605
120832
94757
78875
542756
10061015
10061026
60571
84662
56531
60515
•31113
57976
119937
105679
119904
.99650
51285
121142
606202
123911
122394
**".. 298044
115297
72208
106898
110805
Ingestioo
Beef
Subsistence
farmer
3e-02
4e-03
le+01
36401
4e-fOO
le+01
4e-02
le-W.
le-02
le-02
2e-04
le+03
5e-07
3e-01
16404
le-04
3e-04
36401
2e-01
le-01
36400
36400 .
16400
2e-01
46401
6e-02
96400
4e-01
3e-01
66402
Milk
Subsistence
fanner
9e-03
le-03
46400
96400
16400
46400
le-02
4e-03
4e-03
4e-03
56-05
46402
2e-07
9e-02
46403
36-05
9eX)5
96400
6e-02
4e-02
9e-01
96-01
4e-01
7e-02
16401
2e-02
36400
1.6-01
Se-02
26402
(continued)
August 1995
5-98
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
-' -.- ,
Chemical
Ethyl acetate
Ethyl ether
Ethyl methacrylate
Ethyl methanesulfonate
Ethylbenzene
Ethylene dibromide
Ethylene thiourea
Fluoranthene
Fluorene
Fomaldehyde
Formic acid
Furan
Heptachlor
Heptachlor epoxide
Hexachloro- 1 ,3-butadiene
Hexachlorobenzene
Hexachiorocyclohexane, a- (a-BHC)
Hexachlorocyclohexane, fl- (p-BHQ
Hexachiorocyclohexane, f- (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
Hexachlorophene
Indeno(1^3-cx/) pyrene
Isobutyl alcohol
Isophorone
Kepone
Mercury
Methacrylonitrik
Methanol
Methoxychlor
• . . <
Table 5-5 (continued)
CAS
141786
60297
97632
62500
100414
106934
96457
206440
86737
50000
64186
110009
76448
1024573
67683
118741
319846
319857
58899
77474
67721
70304
193395
78831
78591
'-*5TJ
143500
7439976
126987
67561
72435
'
Beef
Subsistence
farmer
Ie-t03
3c+02
1**02
8e-06
le«02
3e-05
4e-03
6e-K)l
66401
3e-f02
3e403
16400
6e-04
3e-04
3e-02
2e-03
4e-04
le-03
2e-03
16401
2e-01
4e-01
6e-03
46402
36400
5e-05
4e-01
le-01
76402
76400
•
Ingestioo
Milk
Subsistence .
fanner
46402
96401
46401
3e-06
4e401
9e-06
le^)3
26401
26401
9e401
9C402
4e-01
2e-04
9e-05
le-02
5e-04
le-04
4«-04
6c-04
• 3C400
6e-02
le-01
2e-03
16402
8e-01
2e-05
le-01
4e-02
2e402
26400
(continued)
August 199$
5-99
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
-
Chemical
Methyl bromide (bromomethane)
Methyl, chloride (chloromethane)
Methyl ethyl ketone
Methyl isobutyl ketone
Methyl methacrylate
Methyl parathion
Methylcholanthrene, 3-
Methylene bromide
Methylene chloride
Molybdenum
Af-Nitrosodi-fi-propylamine
A/-Nitrosodiphenylamine
JV-Nitrosopiperidine
JV-Nitrosopyirolidine
Naphthalene
Naphthylamine
Nickel
Nitrobenzene
Nitropropane, 2-
Nifrosodi-a-butylamine
Nitrosodiethylamine
Nitrosodimethylamine
Nitrosomethylethylamine
Octamethylpyrophosphoramide
Parathion
Pentachlorobenzene
Pentachloronitrobenzene (PCNB)
Pcntachlorophenol
Phenol
Phenyl mercuric acetate
1 '
Table 5-5 (continued)
CAS
74839
74873
78933
108101
80626
298000
56495
74953
75092
7439987
621647
86306
. 100754
930552
^1203
91598
7440020
98953
79469
924163
55185
62759
10595956
152169
56382
**". 608935
82688
87865
108952
62384
Beef
Subsistence
farmer
2e+00
NA
96402
7C401
16402.
4e-01
le-04
le+01
3e-01
76400
4e-04
5e-01
7e-05
le-03
66401
NA
36401
7e-01
NA
56-04-
2e-05
5e-05
le-04
36400
96400
16400
le-02
2e-02
96402
le-01
Ingestioo
Milk
Subsistence
farmer
6e-01
NA
36402
2C401
4C401
le-01
3e-05
46400
le-01
26400
le-04
2e-01
2e-05
4e-04
26401
NA
96400
2e-01
NA
• ' le-04
5e-06
2e-05
4e-05
9e-0l
36400
4e-01
3e-03
7e-03
36402
4e-02
(continued)
August 1995
5-100
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
\
'
Chemical
Phenylenediamine, m-
Phorate
Polychlorinated biphenyls
Pronamide
Pyrene
Pyridine
Safrole
Selenium
Strychnine
Stryene
TCDD, 2,3.7,8-
Tettachlorobenzene, 1,2,4,5-
Tetrachloroetnane, 1,1,1,2-
Tetrachloroethanc, 1,1,2,2-
Tetrachloroethylene
Tetrachlorophenol, 2,3,4,6-
Tetraethyldithiopyrophosphate
Thallium (I)
Toluene
Toluenediamine, 2,4-
Toluidine, o-
Toluidine, p-
Toxaphene
Trichloro-l,2,2-trifluoroethane, 1,1,2-
Trichlorobenzene, 1,2,4-
Trichloroetnane; 1,1,1-
Trichloroethane, 1,1,2-
TrichloroeUiylene
Trichlorofluoromethane
Trichlorophenol, 2,4^-
table 5-5 (continued)
CAS
108452
298022
1336363
23950585
129000
110861
94597
7782492
57249 .
100425
1746016
95943
630206
79345
'27184
58902
3689245
7440280
108883
95807
95534
106490
8001352
76131
120821
"^ 71556
79005
79016
75694
95954
Beef
Subsistence
fanner
9e+00
3e-01
3c-04
16402
46401
16400
le-02
76400
4e-01
36402
2e-08
4c-01
le-01
le-02
16401
46401
7e-01
le-01
36402
8e-04
le-02
le-02
2e-03
46404
16401
NA
4e-02
2e-01
4e402
16402
Ingestion
Milk
Subsistence
fanner
3C400
9e-02
le-04
36401
16401
4e-01
4e-03
26400
le-01
9e401
5e-09
le-01
3e-02
4e-03
46400
16401
2e-01
4e-02
9C401
2e-p4
3e-03
4e-03
7e-04
16404
46400
NA
le-02
7e-02
16402
46401
(continued)
August 1995
5-101
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
Table 5-5 (continued)
Ingestion
Chemical
Trichlorophenol. 2,4,6-
CAS
88062
Beef
Subsistence
farmer
26-01
Milk
Subsistence
fanner
76-02
Trichlorophenoxyacetic acid. 2.4.5-(245-T) 93765 16401 4e400
Trichlorophenoxypropionic acid, 2,4,5- .
(silvex) 93721 16401 46400
Trichloropropane, 1^,3-
Trinitrobenzene, sym-
Tris (23-
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Intake via Animal Products: Carcinogens— Subsistence Farmer
•'.<-• , . 7K.AT.36
5 «*.!<«»,*«
ED'EF'CSForal
Parameter
I
TR
AT
ED
EF
CSForal
Definition
Intake of contaminant (ug/kg/d)
Target individual risk level (unitless)
Averaging time (yr)
Exposure duration (yr)
Exposure frequency (d/yr)
Oral cancer slope factor (mg/kg/d)'1
Central
Default tendency High-end
value* . value value
Calculated
10-*
.70
20 40
350 . »
Chemical-specific
(5-60)
Refer to
5.2.9.1
5.2.9.1
5.2.9.1
5.2.9.1
52.9.6.3
' Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 1991a).
August 1995
5-103
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
Intake via Animal Products: Noncarcinogens—Subsistence Farmer
(5-61)
Parameter
I
HQ
RfD
Definition
Intake of contaminant (ug/kg/d)
Target hazard quotient (unitiess)
Oral reference dose (mg/kg/d)
Default
value*
1
. Central
tendency High-end
value value
Calculated
Chemical-specific
Refer to
5.2.9.1
5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 199 la).
August 1995 5-104
-------
5.0 EXPOSURE
5.2 Concentrations .for Human Receptors
.Animal Product Concentration—Subsistence Farmer
animal
(5-62)
The above equation was modified slightly for dioxins and PCBs, which tend to
bioaccumulate in lipid material (i.e., fat). (Dioxin document, U.S. EPA, 1992c)
r - **W
Default
Parameter Definition value*
Cwknai Concentration in animal product
0»g/g)
I Intake of contaminant
BW Body weight (kg)
F Fraction contaminated
(ug/kg/d)
70
(unitless)
CR Consumption rate of animal product
(g/d)
Cf- Concentration in animal fat (ug/g)
ffat Fat content of animal
(unitless)
product
Central
tendency High-end
value value
Calculated
From Equations 5-60 and .
5-61
0.4 0.9
57 (beef)
181 (milk)
Calculated
0.22 (beef)
0.035 (milk)
(5-63)
Refer to
5.2.9.1
52.92
52,92
5.2.9.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Sources: RAGS Part B (U.S. EPA, 199 la); Dioxin Document (U.S. EPA, 1994a).
August 1995
5-105
-------
5.0 EXPOSURE 52 Concentrations for Human Receptors
5.2.7 Fish Concentrations
^ ^
Ing
or
«•• w
Fish
Fish concentrations are backcalculated based on ingestion of fish by a fish consumer or
subsistence fisher. The fish consumer is a person with fish consumption rates typical of the
general population who catches contaminated fish. Table 5-6 summarizes the backcalculated
fish concentrations by chemical and receptor.
Starting with a target risk or hazard quotient, the concentration in contaminated fish
ingested by humans is backcalculated in two steps. First, intake associated with the target
risk or hazard quotient is backcalculated. Two different algorithms are used to backcalculate
intake, one for carcinogens and one for noncarcinogens. The concentration in fish is then
backcalculated from the intake. Intake refers to the quantity of contaminant ingested daily,
normalized by body weight
For a fish consumer, Equations 5-64 and 5-65 show the backcalculations of intake for
carcinogens and noncarcinogens, respectively. Equation 5-66 shows the backcalculation for
fish concentration from intake for the fish cr-aumer. Equations 5-67 through 5-69 show the
analogous backcalculations for the subsistence fisher, these differ from the ones for the fish
consumer only in the inputs used.
5.2.7.1 Intake
For carcinogens, exposure is averaged over a lifetime (therefore, the averaging time is
70 years, the average lifetime). Daily intake is backcalculated from the target risk, oral
cancer slope factor, and exposure parameters such as the exposure frequency and duration.
For noncarcinogens, the daily intake is backcalculated by direct comparison to the
reference dose. The reference dose is the dose to which a person may be exposed daily over
a lifetime or significant portion of a lifetime without adverse effect.
• .•=**»
5.2.7.2 Fish Concentration
The concentration in fish is backcalculated from the daily intake of contaminant and
depends on body weight and the consumption rate of freshwater fish.
August 1995 5-106
-------
5.0 EXPOSURE
SJ Concentrations for Human. Receptors
Table 5-6. Exposure Media Concentrations for Fish (mg/kg)
Ingestion
Chemical
Acenaphthene
Acetone
Acetonitrile
Acetophenone
Acrotein
Acrylamide
Acrylonitrile
Aldrin
Allyl chloride
Aniline
Antimony
Arsenic
Barium
Benz(a)anthraceine
Benzene
Benzidine
Benzo(a)pyrene
Benzo(fr)fluoranthene
Benzyl alcohol
Benzyl chloride
Beryllium
Bis(2 le-06
56-04
36-05
26-03
Se-04
4e-03
le-KX)
le-02
36400
le-02
Fish
Subsistence fisher
36-02
6e-02
3e-03
6e-02
le-02
3e-07
2e-06
8e-08
NA
2e-04
26-04
9e-07
4e-02
le-06
5e-05
6e-09
2e-07
le-06
2e-01
8e-06
3e-07
2e-05
le-06
9e-05
2e-05
2e-04
6e-02
66-04
le-01
66-04
(continued)
August 1995
5-107
-------
5.0 EXPOSURE
5J Concentrations for Human Receptors
- -_ ,
Chemical
Carton disulfide
Caibon tetrachloride
Chkxxlane
Chloro-U-butadiene, 2- (chloroprene)
Chloroaniline, p~
Chkxobenzene
Chlorobenzilate
Chlorodibroinomethane
Chloroform -
Chlorophenol, 2-
Cnromium VI
Chrysene
Copper
Cresol, m-
Cresol, a-
Cresol, p-
Curnene
ODD
DDE
DDT
Di-n-butyl phthalate
Di-n-octyl phthalate
Diallate
Dibenz(a,A)anthracene
Dibromo-3-chloropropane, 1,2-
Dichlorobenzene, 1,2-
Dichlorobenzene, 1,4-
Dichlorobenzidine, 33'-
Dichlorodifluoromethane
Dichloroethane, 1,1-
Table 5-6 (continued)
CAS Fhl
75150
56235
57749
126998
106478
108907
510156
124481
67663
95578
7440473
218019
7440504
. 108394
95487
1^445
98828
72548
72559
50293
84742
117840
2303164
53703
96128
95501
'*t 106467
91941
75718
75343
Ingestion
Fish
i consumer
le+00
2e-04
2e-05
.NA
6c-02
3e-01
le-04
4e-04
5e-03
76-02
7e-02
le-03
5e-01
7e-01
7e-01
7e-02
6e-01
le-04
9e-05
9e-05
16400
3e-01
56-04
4e-06
2e-05
16400
le-03
It-OS
36400
4e-04
Fish
Subsistence fisher
6e-02
le-05
le-06
1 NA
2e-03
le-02
5e-06
2e-05
2e-04
3e-03
3e^)3
4e-05
2e-02
36-02
3e-02
3e-03
2e-02
5e-06
4e-06
4e-06
66-02
le-02
2e-05
2e-07
9e-07
56-02
5e-05
3e-06
le-01
le-05
(continued)
August 1995
5-108
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
- — -
Chemical
Dichloroethane, 1 ,2-
Dichloroethylene, 1,1-
Dichloroethylene, cis-\2-
Dichloroethylene, trans-12-
Dichlorophenol, 2,4-
Dichlorophenoxyacetic acid, 2,4- (2,4-D)
Dichloropropane, 12-
Dichloropropene, 13-
Dichioropropenc, CM- 13-
Dichloropropene, /raw- 1,3-
Dieldhn
Dieutyl phthalate
Diethylstilbestrol
Dimethoate
Dimethyl phthalate
Dimethylbenz(a)anthracene, 7,12-
Dimethylbenzidine, 33'-
Dimethylphenol, 2,4-
Dimethyoxybenzidine, 33'-
Dinitrobenzene, 13-
Dinitrophenol, 2,4-
Dinitrotoluene, 2,4-
Dinittotoluene, 2,6-
Dioxane, 1.4-
Diphenylamine
Disulfoton
Endosulfan
Endrur
Epichlorohydrin
Ethoxyethanpl, 2-
Table 5-6 (continued)
V
CAS Fish
107062
75354
156592
156605
120832
94757
78875
542756
10061015
10061026
60571
84662
56531
60515
131113
57976
119937
105679
119904
99650
51285
121142
606202
123911
122394
4* 298044
115297
72208
106898
110805
Ingestion
Fish
consumer
4e-04
5e-05
le-01
3e-01
4e-02
le-01
Se-04
2e4M
2e-04
2e-04
2e-06
le-HOl
It-V)
3e-03
16402
lc-06
3e-06
3e-01
2e-03
le-03
3e-02
3e-02
le-02
3e-03
3e-01
6e-04
Se-02
4e-03'
3e-03
6e-KX)
- •
Fish
Subsistence fisher
le-05
2e-06
6e-03
le4J2
26-03
6e-03
2c-05
7e-06
lt-06
le-06
8e-08
4e-01
3e-10
le-04
66400
5e-08
le-07
le-02
9e^)5
6e-05
lc-03
le-03
6c-04
le-04
le-02
2e-05
' 3e-03
2e-04
le-04
2e-01
(continued)
August 1995
5-109
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Chemical
Ethyl acetate
Ethyl ether
Ethyl methacrylate
Ethyl methanesulfonate
Ethylbenzene
Ethylene dibromide
Ethylene thiourea
Fluoranthene
Fluorene
Fonnaldehyde
Formic acid
Furan
Heptachlor
Heptachlor epoxide
Hcxachloro-1 ^-butadiene
Hexachlorobenzene
Hexachlorocyclohexane, a- (a-BHC)
Hexachlorocyclohexane, p- (0-BHC)
Hexachlorocyclohexane, y (lindane)
Hexachlorocyclopentadiene
Hexachloroethane
Hexachlorophene
Indeno(1.23-c.<0 pyrene
Isobutyl alcohol
Isophorone
Kepone
Mercury
Methacrylonitrile
Methanol
Methoxychlor
Table 5-6 (continued)
CAS Fisl
141786
60297
97632
62500
100414
106934
96457
206440
86737
50000
64186
110009
76448
1024573
87683
11J741
319846
319857
58899
77474
67721
70304
193395
78831
78591
143500
. 7439976
126987
, 67561
72435
Ingestioo
Fish
h consumer
16401
3e-»00
16400
le-07
16400
4e-07
SB-OS :
6e-01
6e-01
3e+00
3e+01
le-02
76-06
4e-06
4e-04
2e-05
5e-06
2e-05
2e-05
le-01
2e-03
4e-03
Se-05
46400
3e-02
7e-07
4e43
le-03.
76400
7e-02
-
'
Fish
Subsistence fisher
5e-01
le-01
Se-02
4e-09
6e-02
2e-08
2e-06
2e-02
2e-02
le-01
16400
6e-04
36-07
le-07
2e-05
8e-07
2e-07
7e-07
le-06
4e-03
' 9e-05
2e-04
3e-06
26-01
le-03
3e-08
. 2e-04
6e-05
3e-01
3e-03
(continued)
August 1995
5-110
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
.... .
Chemical
Methyl bromide (bromomethane)
Methyl chloride (chloromethane)
Methyl ethyl ketone
Methyl isobutyi ketone
Methyl methacrylate
Methyl parathion
Methylcholanthrene, 3-
Methylene bromide
Methylene chloride
Molybdenum
/V-Nitrosodi-n-propylamine
Af-Nitrosodiphenylamine
N-Nitrosopiperidine
Af-Nitrosopyrrolidine
Naphthalene
Naphthylamine
Nickel
Nitrobenzene
Nitropropane, 2-
Nitrosodi-n-butylamine
Nitrosodiethylamine
Nitrosodimethylamine
Nitrosomethylethylamine
Octamethylpyrophosphoramide
Parathion
Pentachlorobenzene
Pentachlotonitrobenzene (PCNB)
Pentachlorophenol
Phenol
Phenyl mercuric acetate
•
Table 5-6 (continued)
CAS Fish
74839
74873
78933
108101
80626
298000
56495
74953
75092 .
7439987
621647
86306
100754
930552
91203
91598
7440020
98953
79469
924163
55185
62759
10595956
152169
56382
^» 608935
82688
87865
108952
62384
Ingestion
Fish
consumer
2e-02
NA .
8e-tOO
7e-01
le-tOO
3e-03
le-06
le-01
4e-0i
7e-02
Se-06
7e4)3
9e^7
2eX)5
fe-01
NA '
3e^)l
7eX)3
NA
6c-06
2e-07
6eX)7
le-06
3C-02
8e-02
le^)2
le-04
3e-04
8e-»00
le-03
Fish
Subsistence fisher
8e-04
NA
3e-01
3e-02
4e-02
le-04
5e-08
6e-03
2e-04
3e-03
2e-07
3e-04
3e^)8
6e-07
2K-02
NA
le-02
3e-04
NA
2e-VJ
9e^09
3e-08
6e-08
le-03
3e-03
4e-04
5e-06
le-05
3e-01
4e-05
(continued)
August 1995
5-111
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Chemical
Phenylenediamine, m-
Phorate
Polychlorinated biphenyls
Pronamide
Pyrene
Pyridine
Safrole
Selenium
Strychnine
Stryene
TCDD, 2.3,7,8-
Tetrachlorobenzene, 1,2,4,5-
Tetrachloroethane, 1,1,1,2-
Tetrachloroethane, 1,1,2,2-
Tetrachloroethylene
Tetrachtorophenol, 2.3,4,6-
Tetraethyldithiopyrophosphate
Thallium (I)
Toluene
Toluenediamine, 2,4-
Toiuidine, o- .
Toluidine, p-
Toxaphene
Trkhloro-l,2,2-trifluoroethane, 1,1,2-
Trichlorobenzene, 1,2,4-
Trichloroethane, 1,1,1-
Trichloroethane, 1,1.2-
Trichloroethylene
Trichlprofluorometnane
Trichlorophenol, 2,4^-
Table 5-6 (continued)
CAS Fisl
108452
298022
1336363
23950585
129000
110861
' 94597
7782492
57249
100425
1746016
95943
630206
79345
127184
3U902
3689245
7440280
108883
95807
95534
106490
8001352
76131
120821
^ 71556
79005
79016
75694
95954
/
/ ,
Ingestion
Fish
h consumer
8e-02
3e-03
4e-06
16400
4e-01
le-02
2e-04
7e-02
4e-03
3e*00
2e-10
4e-03
le-03
2e-04
le-01
4e-01
7e-03
le-03 -
3C+00 ,
le-05 .
le-04
2e-04
3er05
4e*02
le-01
NA
6C-04
3e-03
4e-*00
le-fOO
Fish
Subsistence fisher
3e-03
le-04
2e-07
4e-02
2e-02
6e-04
7e-06
3e-03
2e-04
le-01
8e-12
2e-04
5e-05
7e-06
6e-03
2e-02
3e-04
4e-05
le-01
4e-07
5e-06
• 7e-06
le-06
2e-»01
6e-03
NA
2e-05
le-04
2e-01
6e-02
(continued)
August 1995
5-112
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-6 (continued)
Ingestioo
Fish
Fish
Chemical
CAS
Fish consumer
Subsistence fisher
Trichlorophenol. 2,4,6-
88062
3e-03
le-04
Trichlorophenoxyacetic acid, 2,4.5-(245-T)
93765
le-01
Trichlorophenoxypropionk acid, 2,4.5- (silvex)
Trichloropropane. 1.2,3-
Trinitrobenzene, sym-
Tris (23-dibromopropyl) phosphate
93721
96184
99354
126727
le-01
Se-02
7e-04
3e-06
4e-03
3e-03
3e-05
le-07
Vanadium
7440622
le-01
4e-03
Vinyl chloride
Xyienes (total)
75014
i3"30207
2e-05
le-fOO
Zinc
7440666
2e-01
NA = Not applicabte.
August 1995
5-.113
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Jntake via Fish Digestion: Carcinogens—Fish Consumer
TR»AT*365d/yr
ED»EF*CSForal
(5-64)
Parameter
I
TR
AT
ED
EF
CSFbral
Definition
Intake of contaminant (ug/kg/d)
Target individual risk level (unitless)
Averaging time (yr)
Exposure duration (yr)
Exposure frequency (d/yr)
Oral cancer slope factor (mg/kg/d)'1
Default
value*
10-*
70
350
Central
tendency High-end
value value
Calculated
9 30
,
Chemical-specific
Refer to
5.2.9.1
5.2.9.1
5.2.9.1
5.2.9.1
5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 1991a).
August 1995
5-114
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Intake via Fish Ingestion: Noncarcinogens—Fish Consumer
/ = HQ'RfD
(5-65)
Parameter
Definition
Central
Default tendency High-end
value* value value
Refer to
I
HQ
Rfl>
Intake of contaminant (mg/kg/d)
Target hazard quotient (unitless)
Oral reference dose (mg/kg/d)
Calculated
1
Chemical-specific
5.2.9.1
5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Pan B (U.S. EPA, 199la).
August 1995
5-115
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Fish Concentration — Fish Consumer
I*BW
fuh
(5-66)
Parameter
Definition
Central
Default tendency High-end
value* value value
Refer to
Cfuh
I
BW
F
CR
Concentration in fish (mg/g)
Intake of contaminant (mg/kg/d)
Body weight (kg)
Fraction contaminated (unitless)
Consumption rate of fish (g/d)
Calculated
From Equations 5-64
and 5-65
70
1
1.6 5.3
5.2.9.1
5.2.9.2
5.2.9.2
* Default values are standard default used in Agency risk'assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 199la).
August 1995
5-116
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Intake via Fish Digestion: Carcinogens—Subsistence Fisher
= TR*AT*365d/yr
~ ED»EF*CSForal
(5-67)
Parameter
I
TR
AT
ED
EF
CSForal
t
Definition
Intake of contaminant (pg/kg/d)
Target individual risk level (unitless)
Averaging time (yr)
Exposure duration (yr)
Exposure frequency (d/yr)
Oral cancer slope factor (mg/kg/d)*1
Default
value*
ID"6
70
350
Central
tendency High-end
value value
Calculated
9 30
*-
Chemical-specific
Refer to
5.2.9.1
5.2.9.1
5.2.9.1
5.2.9.1
52.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 199 la). .
August 1995
5-117
-------
5.0 EXPOSURE SJ Concentrations for Human Receptors
Intake via Fish Ingestion: Noncarcinogens — Subsistence Fisher
/ = HQ'RfD C5'68)
Parameter
I
HQ
RfD
Default
Definition value*
Intake of contaminant (mg/kg/d)
Target hazard quotient (unitless) 1
Oral reference dose (mg/kg/d)
Central
tendency High-end
value value
Calculated
Chemical-specific
Refer to
•
5.2.9.1
5.2.9.6.3
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA. 199 la).
August 1995 5-118
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Fish Concentration—Subsistence Fisher
fah
I*BW
(5-69)
Parameter
Definition
Central
Default tendency High-end
value* value value
Refer to
Co*
I
BW
F
CR
Concentration in fish (mg/g)
Intake of contaminant (mg/kg/d)
Body weight (kg)
Fraction contaminated (unitless)
Consumption rate of fish (g/d)
Calculated
From Equations 5-67
and 5-68
. 70.
1 '•'.''
60 130
5.2.9.1
52.9.2
5.2.9.2
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: RAGS Part B (U.S. EPA, 199 la).
August 1995
5-119
-------
5.0 EXPOSURE 52 Concentrations for Human Receptors
5.2.8 Breast Milk Exposure
Infants may be exposed to contaminants through breast milk if the nursing mother has
been exposed. This is-particularly of concern for dioxin-like compounds, which are highly -
lipophilic, and likely to accumulate in breast milk; however, this may be offset by the limited
nature of the exposure, typically only 1 year.
The infant's exposure is calculated in two steps. First, the concentration in breast milk
fat is calculated from the mother's exposure. Next, the average daily dose for the infant is
calculated. Good input data are available for these calculations only for dioxin. Equations
5-70 and 5-71 present these calculations for dioxin.
For this analysis, dioxin exposure to the infant through this route was compared to the
maternal exposure to assess what effect using an infant receptor instead of an adult receptor
would have for any of the pathways considered in this analysis. Using the inputs shown in
Equations 5-70 and 5-71, the infant's average daily dose (ADD) is about 32 times the
mother's ADD. Equation 5-72 shows the calculation of lifetime average daily dose averaged
over a 70-year lifetime (LADD) from average daily dose. Assuming that the mother is
exposed 350 d/yr and the infant is exposed 365 d/yr, and given exposure durations of 1 year
for the infant (a typical length of time infants are breast-fed) and 30 years for the mother, the
lifetime average daily dose averaged over 70 years for the infant is approximately equal to
that of the mother. .
These results suggest that inclusion of infant exposure to breast milk for dioxin in any
of the pathways would produce similar results to using an adult receptor if only breast milk
exposure were considered. If both breast milk exposure and adult exposure to the same
person were considered, the risk to the receptor would be approximately doubled, leading to
an exit criterion for dioxin of about one-half of the exit criterion calculated for adult exposure
alone.
August 1995 . 5-120
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Lifetime Average Daily Dose
LADD
(5-70)
Parameter
LADD
ADD
ED
EF
AT
Definition
Lifetime average daily dose
(pg/kg/d)
Average daily dose (pg/kg/d)
Exposure duration (yr)
Exposure frequency (d/yr)
Averaging time (yr)
Default
value*
Central
tendency
value
High-end
value
Refer to
Calculated
See Equation 5-71
1 (infant)
350 (mother)
365 (infant)
70
9 (mother)
30 (mother)
5.2.9.1
5.2.9.1
5.2.9.1
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dioxin Document (U.S. EPA, 1994a).
August 199S
5-121
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Average Daily Dose
(5-71)
Parameter Definition
ADDjnfc,,, Average daily dose to the infant (pg/kg/d)
c«uk&» Concentration in maternal milk (pg/kg of
milkfat)
IR^ Ingestion rate of breast milk (kg/d)
BWin&nt Bo
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Concentration of Dioxin in Maternal Milk
0.693 »/2
(5-72)
Parameter
C-,.
ADD.^
h
ft
*2
Default
Definition value*
Concentration in maternal milk (pg/kg of
milkfat)
Average maternal intake of dioxin (pg/kg
BW/d)
Half-life of dioxin (d)
Proportion of ingested dioxin that is stored 0.9
in fat
Proportion of mother's weight that is fat (kg 0.3
maternal fat/kg total body weight)
Central
' tendency High-end
value value
Calculated
Pathway-dependent
2,555
Refer to
52.9.5
52.9.5
52.9.5
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
Source: Dioxin Document (U.S. EPA, 1994a).
August 1995
5-123
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
5.2.9 Human Exposure Inputs
Table 5-7 summarizes the exposure assumptions used by exposure scenario. Each type
of parameter (e.g., exposure duration, intake) is discussed in the following sections.
5.2.9.1 General Exposure Parameters
The general exposure parameters include target risk, target hazard quotient, averaging
time, body weight, exposure duration, and exposure frequency.
5.2.9.1.1 Target Risk/Hazard Quotient
The target risk is set to 10~6 and the target hazard quotient is set to 1 for this
assessment The media concentrations calculated are directly proportional to the risk level
used and indirectly proportional to the hazard quotient used; therefore, any change in these
values would have an equal effect on the calculated media concentrations.
5.2.9.1.2 Averaging Time
A 70-year lifetime averaging period is considered a standard value and corresponds to
assumptions made in developing cancer slope factors.
5.2.9.1 J Body Weight
An average adult body weight value ot 70 kg is a standard value presented in the
Exposure Factors Handbook (U.S. EPA, 1990d). This value is based on experimental studies
and reflects the average weight of adult males. For children, the average of 50th percentile
for male and female children was used. For children 1 to 6 years old, this was 15 kg.
5.2.9.1.4 Exposure Duration
Table 5-8 summarizes the exposure durations used. The exposure duration reflects the
length of time the exposed individual resides or works near or on the waste management unit
The Exposure Factors Handbook (U.S. EPA, 1990d) recommends a central tendency exposure
duration value of 9 years and a high-end duration of 30 years. This reflects the average and
90th percentile of the time a person has occupied his or her current residence. These values
were used for most scenarios and reosgjors, including adult resident, home gardener, fish
consumer, and subsistence fisher.
For the child resident receptor, the exposure duration is limited to the number of years
a child could fall into the age range considered (1 to 6 years). This age range suggests an
exposure duration of 6 years for the child resident receptor.
For the child-to-adult receptor for soil ingestion, the total exposure duration was set to
9 or 30 years. The first 6 years were then assigned to the child phase of exposure, and the
August 1995 5-124
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-7. Summary of Exposure Inputs by Scenario
t
Parameter
Inhalation of Air— Adult Resident
Intake rate of air (m3/d)
Exposure duration for. inhalation of air (yr)
Exposure frequency for inhalation of air (d/yr)
Inhalation of Air— Worker
Intake rate of air (m3/d)x
Exposure duration for inhalation of air (yr)
Exposure frequency for inhalation of air (d/yr)
Ingestion of Drinking Water— Adult Resident
Intake of drinking water (L/d)
Exposure duration for ingestion of drinking water (yr)
Exposure frequency for ingestion of drinking water (d/yr)
Ingestion of Soil— Child+Adult Resident
Intake of soil (g/d) Adult
Child (1-6)
Exposure duration for ingestion of soil (yr) Adult
Child (1-6)
Exposure frequency for ingestion of soil (d/yr) Adult
Child (1-6)
Ingestion of Crops — Home Gardener
Intake of root vegetables (g/d)
Intake of fruits and aboveground vegetables (g DW/d)
Fraction of root vegetables ingested growrS^n contaminated soil
(unitless)
Fraction of fruits and aboveground vegetables ingested grown in
contaminated soil (unitless)
Exposure duration for ingestion of crops (yr)
Exposure frequency for ingestion of crops (d/yr)
Aumist 1995
Default Central High
value* tendency end
20
9 30
350
>
20
9 25
250
1.4 2.0
9 30
350
0.1
0.2
3 24
6
350 ' . .
350
28
19.7
025 0.40
025 0.40'
9 30
350
(continued)
5-125
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-7 (continued)
Parameter
Default Central High
value* tendency end
Ingestion of Crops—Subsistence Fanner
Intake of root vegetables (g/d) 28
Intake of Emits and aboveground vegetables (g DW/d) 19.7
Fraction of root vegetables ingested grown in contaminated soil 0.4 0.9
(unitless) •
Fraction of fruits and aboveground vegetables ingested grown in 0.4 0.9
contaminated soil (unitless) • , '
Exposure duration for ingestion of crops (yr) 20 40
Exposure frequency for ingestion of crops (d/yr) 350
Ingestion of Animal Products—Subsistence Fanner
Intake of beef (g/d) ' . 57
Intake of milk (g/d) 181
Fraction of beef ingested originating in contaminated soil (unitless) 0.4 0.9
Fraction of milk ingested originating in contaminated soil (unitless) 0.4 0.9
Fat content of beef (unitless) ."• 022
Fat content of milk (unitless) 0.035
Exposure duration for ingestion of animal products (yr) 20 40
Exposure frequency for ingestion of animal products (d/yr) 350
Ingestion of Fish—Fish Consumer
Intake of fish (g/d) • j 1.6 5.3_
Fraction fish contaminated (unitless) 1
Exposure duration for ingestion of fish (yr) 9 30
Exposure frequency for ingestion of fish (d/yr) 350
Ingestion of Fish—Subsistence Fisher «*»
Intake of fish (g/d) 60 130
Fraction fish contaminated (unitless) 1
Exposure duration for ingestion of fish (yr) 9 30
Exposure frequency for ingestion of fish (d/yr) * 350
(continued)
August 1995
5-126
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table 5-7 (continued)
Parameter
Default
value*
Central
tendency
High
end
Bathing Dermal Contact with Water—Adult Resident
Exposed skin surface area (cm2) 20,000
Bathing contact time (h/event) 0.17 0.25
(10 min) (15 min)
Exposure duration for bathing dermal contact with water (yr) 9 30
Exposure frequency for bathing dermal contact with water (d/yr) 350
Event frequency for bathing (events/d) 1
Bathing Dermal Contact with Water—Child Resident _ '
Exposed skin surface area (cm2) 6,900
Bathing contact time (h/event) 0.17 0.33
(10 min) (20 min)
Exposure duration for bathing dermal contact with water (yr) 6
Exposure frequency for bathing dermal contact with water (d/yr) 350
Event frequency for bathing (events/d) ' 1
Dermal Contact with Soil—Adult Resident
Dermal adherence factor (mg soil/cm2 event) 02 1.0
Particle density of soil (g/cm3) 2.65
Exposed skin surface area (cm2) 5.000
Skin boundary layer thickness (cm) 0.5
Dermal soil contact time (h/d) 8 '
Exposure duration for dermal contact with soil (yr) 9 30
Exposure frequency for dermal contact with soil (d/yr) . 40 350
Dermal Contact with Soil—Worker
Dermal adherence factor (mg soil/cm2 event) ' 0.2 1.0
- - •••-*» -. '•-.
Panicle density of soil (g/cm3) - 2.65 .
Exposed skin surface area (cm2) 2,400
Skin boundary layer thickness (cm) 0.5
Dermal soil contact time (h/d)
8
(continued)
August 1995
5-127
-------
5.0 EXPOSURE 5J Concentrations for Human Receptors
Table 5-7 (continued)
Parameter
Exposure duration for dermal contact with soil (yr)
Exposure frequency for dermal contact with soil (d/yr)
Dermal Contact with Soil— Child Resident
Dermal adherence factor (mg soil/cm2 event)
Particle density of soil (g/cm3)
Exposed skin surface area (cm2)
Skin boundary layer thickness (cm)
Dermal soil contact time (h/d)
Exposure duration for dermal contact with soil (yr)
Exposure frequency for dermal contact with soil (d/yr)
Miscellaneous
Average body weight (kg) ' Adult
Child (1-6)
Lifetime/averaging time for carcinogens (yr)
Target risk (unitless)
Target hazard quotient (unitless)
Default Central
value* tendency
9
250
- 02
2.65
1,700
0.5
5
6
130
70
15
70
KT6
i
High
end
25
1.0
12
350
Default values are standard default used in Agency risk assessments or policy used in this rule-making.
August 1995 5-128
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
Table 5-8. Summary of Exposure Duration Values (years)
Receptor
Adult resident
Home gardener
Subsistence fisher
Child resident
Subsistence fanner
Worker
Default value* Central tendency
9
. . . . • 0
" 9
6
20
9
High end
30
30
30
40
25
• Default values are standard default used in Agency risk assessments or policy used in this rule-making.
remaining years (3 or 24) were assigned to the adult phase of exposure, as recommended by
the Exposure Factors Handbook (U.S. EPA, 1990d).
*• . • '
For farmers, the Dioxin document (U.S. EPA, 1994) suggests central tendency and
high-end exposure durations of 20 and 40 years, reflecting the fact that farmers move less
frequently than the general population.
—#••
For workers, the Risk Assessment Guidance for Superfund (RAGS): Volume I—Human
Health Evaluation Manual (Pan B) (U.S. EPA, 199la) suggests a high-end exposure duration
of 25 years. .
5.2.9.1.5 Exposure Frequency
Table 5-9 summarizes the exposure frequency values used. The exposure frequency for
most scenarios was 350 d/yr. This is a standard default value recommended in the Human
Health Evaluation Manual Supplemental Guidance: Standard Default Exposure Factors (U.S.
EPA, 1991b), which accounts for the exposed individual being away from the contaminated
environment for a period of 15 d/yr. This was used for most receptors and scenarios,
including the adult resident, child resident, subsistence farmer, fish consumer, and subsistence
fisher receptors for all scenarios exc^gt dermal soil contact ' •
• For workers, the exposure frequency is set to 250 d/yr, reflecting that a full-time worker
usually works 5 d/wk, 50 wk/yr. For a home gardener, the exposure frequency is set to 175
d/yr, reflecting that gardening does not occur year-round, and that home gardeners are
unlikely to'can produce for winter consumption. .
For dermal soil contact, the Dermal document (U.S. EPA, 1992d) recommends a central
tendency exposure frequency value of 40 d/yr, a typical value for-a person who works outside
August 1995 • 5-129
-------
5.0 EXPOSURE 52 Concentrations for Human Receptors
Table 5-9. Summary of Exposure Frequency Values (days/year)
Default Central High
Receptor Scenario value* tendency end
Adult resident Air inhalation 350
Soil ingestion
Dermal bathing
Home gardener
Fish consumer
Subsistence fisher
Child resident
Dermal soil
Crop ingestion
Fish ingestion
Fish ingestion
Soil ingestion
Dermal bathing
Dermal soil
40 350
175
350
350
350
130 350
Subsistence fanner Crop ingestion 350
BeefAnilk ingestion
Worker Air inhalation 250
Dermal soil
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
or in a garden one or two times a week during the warmer months of the year. This was
used for an adult resident for this scenario. The central tendency value of 130 d/yr for
children is an estimate based on 6 d/wk, 5 mo/yr. The high-end value for both children and
adults was selected to be consistent with the standard default exposure frequency
recommended in U.S. EPA (1991b).
5.2.9.2 Intakes/Fraction Contaminated
Table 5-10 summarizes the intakes and fractions contaminated used for this analysis.
«3to
5.2.9.2.1 Air !
A high-end air intake rate of 20 m3/d was used for this exposure scenario for both the
adult resident and the on-site worker. For the adult resident, this value was based on 8 h/d at
resting activity level (0.7 m3/h) and 16 h/d at light activity level (0.8 m3/h) for male adults,
and rounded to one significant figure. Adult males were used to correspond to the standard
body weight, which is based on adult males. For the on-site worker, this value was based on
:. " ' V
August 1995 • 5-130
-------
Table 5-10, Summary of Intakes and Fraction Contaminated
for Ingestion Scenarios
Media
Air
Soil
.1
Drinking water
Aboveground fruits
and vegetables
Root vegetables
Beef
Milk
Fish
'
Receptor
Adult resident
Worker-
Child resident
it Adult resident
Adult resident
Home gardener
Subsistence fanner
Home gardener
Subsistence fanner
Subsistence fanner
Subsistence fanner
Fish consumer
Subsistence fisher
Default
value*
20 m3/d
20 m3/d
200mg/d
100 mg/d
Intake
Central
tendency High end
'
1.4 yd 2 Ud
:9.7gDW/d
19.7gDW/d
28 g/d
28g/d
57 g/d
181 g/d
1.6 g/d 5.3 g/d
60 g/d 130 g/d
Fraction <
Default Central
value" tendency
0.25
0.4
0.25
0.4
0.4
0.4
1
1
contaminated
i
High end
NA
NA
NA
NA
NA
0.4
0.9
0.4
0.9
0.9
0.9
NA = Not applicable.
* Default values are standard default used in Agency risk assessments or policy used in this rule-making.
M
•X
•O.
en
n
o
I
0
3*
w^
I
•u
3
3
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
8 h/d at moderate activity level (2.5 m3/h). Hourly air intake rates for different activity levels
were taken from-the Exposure Factors Handbook (U.S. EPA, 1990d).
5.2.9.2.2 Soil
The soil intake rates for both the adult and the child are standard default values. The
soil ingestion rate of 100 mg/d for an adult reflects inadvertent soil ingestion resulting from
soil ingestion occurrences such as ingesting food products coated with soil residue. The soil
intake rate of 200 mg/d for a child also reflects inadvertent soil consumption during outdoor
activities; it does not reflect pica behavior.
5.2.9.2 J Drinking Water
The central tendency and high-end drinking water consumption rates of 1.4 and 2.0 L/d
are based on average and 90th percentile values recommended by the Exposure Factors
Handbook (U.S. EPA, 1990d). A consumption rate of 2 L/d corresponds to 8.5 cups of water
per day..
5.2.9.2.4 Crops
The Exposure Factors Handbook (U.S. EPA, 1990d) gives total fruit consumption as
140 g/d (whole weight) and total vegetable consumption (including both aboveground and
root vegetables) as 200 g/d (whole weight). For this analysis, consumption of unprotected
fruits and unprotected aboveground vegetables is needed for the aboveground fruits and
vegetables category, and consumption of unprotected root vegetables is needed for the root
vegetable category.
Based on data from Pao et al. (1982), the Dioxin document (U.S. EPA, 1994) breaks
fruits into protected and unprotected fractions and vegetables'into above- and belowground
Table 5-11. Summary of Fruit and Vegetable Consumption Data.
Percent Percent
Category Total consumption protected unprotected
Fruits 140 g/d 56% 44%
Vegetables 200 g/d
Aboveground 25% 54%
Root 1% 20%
August 1995 5-132
-------
5.0 EXPOSURE 5.2 Concentrations for Human Receptors
protected and unprotected fractions, as shown in Table 5-11. Using these fractions, consump-
tion of unprotected-fruit is 88 g/d; consumption of unprotected aboveground vegetables is 76
g/d, and consumption of unprotected root vegetables is 28 g/d.
For aboveground fruits and vegetables, consumption on a dry weight basis is needed.
Due to differences in the algorithms for root vegetables, whole weight consumption for root
vegetables is needed. Therefore, the consumption rate of 28 g/d for root vegetables is used
directly, but the consumption rates for fruits and aboveground vegetables are converted to dry
weight Baes et al. (1984) present wet-to-dry conversion factors for 8 fruits and 16 above-
ground vegetables; these are summarized in Table 5-12. These were averaged to obtain an
average wet-to-dry conversion of 0.15 for fruits and 0.085 for aboveground vegetables.
Applying these to the whole weight consumption rates shown (88 and 76 g/d, respectively)
results in dry weight consumption rates of 13.2 g DW/d and 6.5 g DW/d for fruits and above-
ground vegetables, respectively. These were summed to arrive at an overall consumption rate
for fruits and aboveground vegetables of 19.7 g DW/d.
The aboveground fruits and vegetables consumption rate is equivalent to about three 1-
cup servings of chopped spinach per day. The root vegetable consumption rate is equivalent
to eating approximately one-third of a medium-sized raw carrot per day.
These consumption values are central tendency values and are used for both home
gardeners and farmers and are not varied. .Variation in intake of contaminated fruits and
vegetables is accounted for by using a high-end value for the fraction of fruits and vegetables
consumed that are contaminated. For home gardeners, the Exposure Factors Handbook
recommends a high-end value for the contaminated fraction of the diet to be 0.40. A farmer's
garden plot is likely to be larger than a home gardener's garden plot; therefore, it follows that
the fraction of ingested fruits and vegetables grown on contaminated soil is also greater for
the farmer than for the home gardener. The Exposure Factors Handbook does not suggest a
value for fanners; however, other EPA risk assessments (e.g., Pulp and Paper, U.S. EPA,
1990b) have used 0.9 as a high-end value. The central value for farmers was assumed to be
the same as the high-end value for home gardeners (0.4).
5.2.9.2.5 Beef and Milk
The U.S. Department of Agriculture Nationwide Food Consumption Survey (USDA,
1993) found an average beef consumption rate (including fat) of 57 g/d (whole weight) and a
fresh milk consumption value, whicMflcludes milk fat, of 181 g/d for adults over age 20.
The beef consumption rate is equivalent to 2 ounces of beef per day, or about half of a
quarter-pound hamburger. The milk ingestion rate is approximately equal to 6 ounces (or 3/4
cup) of milk per day. .
These consumption values are central tendency values and are not varied. Variation in
intake of contaminated beef and milk is accounted for by using a high-end value for the
fraction of beef and milk consumed that is contaminated. The Exposure Factors Handbook
August 1995 5-133
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-12. Summary of Wet-to-Dry Conversion Factors Used for
Fruits and Aboveground Vegetables
Vegetables
Asparagus
Snap beans
Cucumber
Eggplant
Sweet pepper
Squash
Tomato
Broccoli
Brussels sprouts v
Cabbage
Cauliflower
Celery
Escarole
Green onions
Lettuce
Spinach green
Average for vegetables .
0.070
b.in
0.039
0.073
0.074
0.082
0.059
0.101
0.151
0.076
0.083
0.063
0.134
0.124
0.052
0.073
0.085
Fruits
Apple
Bushbeny
Cherry
Grape
Peach
Pear
Strawberry
Plum/prune
Average for fruits*
•.•
0.159
0.151
0.170
0.181
0.131
0.173
0.101
0.540
0.15
* Plum/prune was omitted from the average as an outlier.
suggests a central value of 0.4, but no high-end value. However, other EPA risk assessments
(e.g., Pulp and Paper, U.S. EPA, 1990b) have used 0.9 as a high-end value.
«**»
5.2.9.2.6 Fish
The USD A (1987-1988) recommends a high-end fish consumption rate for the. general
population of 5.3 g/d, which corresponds to eating approximately one fish filet a month. The
central fish consumption rate is 1.6 g/d. Both of these fish consumption rates correspond to .
consumption of finfish from fresh waterbodies. To reflect a fish consumer, the fraction
contaminated is set to 1.
August 1995
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5.0 EXPOSURE
5.2 Concentrations for Human Receptors
For subsistence fishers, the consumption rates are based on a survey of subsistence
fishers in the Columbia River basin (Columbia River Inter-Tribal Fish Commission, 1994).
This survey gives an average value of 60 g/d and a high-end value of 130 g/d. A value of 1
for fraction contaminated is assumed based on the subsistence fisher scenario.
5.2.9.3 Miscellaneous Food Chain Parameters
5.2.9.3.1 Fat Content of Beef and Milk
The lipophilic characteristics of dioxin-like compounds causes them to accumulate in
the lipid portion of beef and milk rather than be distributed throughout the entire media.
Therefore, the fat contents of beef and milk were required to calculate the total concentration
present in beef and milk for dioxin-like compounds. Typical fat content values of 0.22 for
beef and 0.03S for milk were obtained from the Dioxin document (U.S. EPA, 1992c). These
are central tendency values 'and are not varied.
5.2.9.4 Dermal Exposure Parameters
5.2.9.4.1 Skin Surface Area
Exposed skin surface area is needed for the dermal scenarios. Total skin surface area
varies with age. The portion that is exposed depends on the exposure scenario. Table 5-13
summarizes the skin surface area values used for each receptor and scenario.
Typical total skin surface areas for adults, children 1 to 6 years old and children 6 to 12
years old are estimated based on regression equations and NHANES n height and weight
data. For adults, the 50th percentile for adult males was used to correspond to the body
weight of 70 kg used (which is a standard value derived from adult males). For children 1 to
Table 5-13. Summary of Skin Surface Area Values
Receptor
Adult resident
.
Child aged 1-6
Worker
Scenario
Dennal bathing
Dennal soil
Dennal bathing
Dennal soil
Dennal soil
Total area
(cm2)
20,000
* 20,000
6,900
6,900
20,000
Percent exposed
(percent)
100%
25%
100%
25%
12%
Exposed area
(cm2)
20,000
5,000
6,900
1,700
2.400
August 1995
5-135
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5.0 EXPOSURE 52 Concentrations for Human Receptors
6 years old* 50th percentile values for male and female children aged 2 to 6 years were
averaged. Data are not available on 1-year-old children; therefore, this value may slightly
overstate the average skin surface area of children 1 to 6 years old. The percentile values for
each age are presented in the Dermal document, but the regression equations are not
presented. Because the skin surface area is based in part on body weight, and because
standard default body weights that reflect average values are used in this analysis, only
typical skin surface areas are used as well.
. For bathing scenarios, it is assumed that 100 percent of the skin surface area is
exposed.
For dermal soil exposure, the adult and child resident receptors are assumed to be
wearing shorts, a short-sleeved shirt, and shoes, leaving the head, hands, forearms, and lower
legs exposed. The Dermal document presents data on the percent of total body surface area
attributable to a variety of body parts for adults. Using those values, the scenario described
leaves about 25 percent of the skin surface area exposed. This percentage is applied to the
total skin surface areas for adults and children ages 1 to 6 years to estimated exposed skin
surface area for the dermal soil scenarios. Workers are assumed to be wearing long pants, a
short-sleeved shirt, gloves, and shoes, leaving the head and forearms exposed. These body
parts account for about 12 percent of total skin surface area.
5.2.9.4.2 Event Frequency
Dermal exposure to water is calculated based on exposure events rather than for a
specific time period; therefore, the number of exposure events per day is needed for the
dermal bathing scenario. Tarshis (1981), cited in the Dermal document (U.S. EPA, 1992d),
found that approximately 90 percent of all Americans bathe once a day. Therefore, an event
frequency of 1 per day was used for the bathing scenario. Because less than 10 percent of
the population bathes more frequently than once a day, no high-end value is considered for
this analysis.
5.2.9.4 J Contact Times
Dermal exposure calculations require an estimate of the contact time per event (for
water exposures) or per day (for soil exposures). Table 5-14 summarizes the data on contact
time.
«*B •
Bathing contact times for adults are based on 50th and 90th percentiles for showering
duration from James and Knuiman (1987) (7 and 12 minutes, respectively) and allowing "a •
few minutes for water residues to dry" (Dermal document, U.S. EPA, 1992d). No data were
available for bathing times for children 1 to 6 years old, who are assumed to take baths
instead of showers. Central trendency and high-end estimates were made for children 1 to 2
years old (who are likely to be bathed by a parent) and for 3- to 6-year-olds (assuming they
bathe themselves and allowing for play time). These estimates were 5 to 10 minutes for 1- to
2-year-olds, and 10 to 20 minutes for 3- to 6-year-olds. Central and high-end estimates for
August 1995 5-136
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5.0 EXPOSURE 52 Concentrations for Human Receptors
- Table 5-14. Summary of Dermal Contact Time Values
Scenario
Bathing
Soil
Receptor
Child resident
Adult resident
Child resident
Adult resident
Worker
Central tendency
10min»
(0.17 h)
10 min
(0.17 h)
5h
8h
High end
20minb
(0.33 h)
IS min
(0.25 h)
1.2 h
* Based on a time-weighted average of central tendency estimates for 1- to 2-year-olds (5 minutes) and 3-
to 6-year-olds (10 minutes), using weights of 2 and 4, respectively, reflecting the number of years in
each age group. The result (8.3 minutes) was rounded up to account for drying time.
b Based on a time-weighted average of high-end estimates for 1- to 2-year-olds (10 minutes) and 3- to 6-
year-olds (20 minutes), using weights of 2 and 4, respectively, reflecting the number of years in each age
group. The result (17 minutes) was rounded up to account for drying time.
the whole 1- to 6-year age range were calculated based on a weighted average across the two
age ranges (weighted by number of years in <"»ch age range) and rounded up to allow for
drying time.
There are few data on contact times for dermal soil exposure. Fortunately, the dermal
soil exposure calculations are fairly insensitive to contact time. The Dermal document cites
data from a study by Hawley (1985). A central estimate reflecting average play time
outdoors for "older" children is 5 h/d; a high-end value, based on 2-1/2-year-olds and
reflecting the fact that soil may stay on the skin after returning indoors, is 12 h/d. Although
these values are not based on the same underlying assumptions, no other data were found.
Therefore, they are used for children. For adults, the value of 8 hours represents a typical
work day for the worker. This value is also used for the adult resident, for lack of better
data.
5.2.9.4.4 Adherence Factor >«**»•
The adherence factor represents the tendency of soil to stick to the skin. The Dermal
document (U.S. EPA, 1992d) reviews a variety of experimental studies on the dermal
adherence factor. Based on these studies, the Dermal document suggests default central
tendency and high-end values of 0.2 and 1 mg/cm2, respectively. The adherence studies are
independent of time; therefore, the Dermal document recommends interpreting the adherence
factor on an event basis.
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5.0 EXPOSURE 5.2 Concentrations for Human Receptors
5.2.9.4.5 Skin Boundary Layer Thickness
The skin boundary layer thickness refers to the unmixed layer of air over the skin
through which chemicals move by means of molecular diffusion. As suggested in the Dermal
document, a single skin boundary layer thickness value of 0.5 cm was used.
5.2.9.4.6 Particle Density of Soil
The value used for particle density of soil, 2.65 g/cm3, is a standard value for any
mineral material. It is applicable to soil, sludge, and ash.
5.2.9.5 Breast Milk Exposure Parameters
All of the input values used for the breast milk calculations are from the Dioxin
document (U.S. EPA, 1994). There is little discussion of the input values in that source. The
half-life of dioxin is estimated to be 7 years or 2,555 days. The values for proportion of
ingested dioxin stored in fat (f}), proportion of mother's weight that is fat (f2), ingestion rate
of breast milk, fraction fat in breast milk (f3), and fraction of ingested dioxin that is absorbed
(£4) are from Smith (1987). Infant body weight is from National Center for Health Statistics
(average of 6 to 11 months and 1 year).
5.2.9.6 Chemical-Specific Parameters
• • . •
The chemical-specific data are presented in Appendix A. This section describes how
values were obtained for each parameter. SJvtion 5.2.9.6.1 describes the physical-chemical
properties of the chemicals. Section 5.2.9.6.2 describes the chemical-specific dermal
parameters, and Section 5.2.9.6.3 describes the health benchmarks.
5.2.9.6.1 Physical-Chemical Properties
Physical-chemical properties were not varied. Best estimates of the values were set for
some properties, while some properties were calculated from one or more of the other
properties. The following values were set to best estimates:
• Diffusivity in air
• Vapor pressure
• Solubility •*»
• Molecular weight <
• Octanol-water partition coefficient (K<,w).
The following properties were calculated from one or more of the other properties:
Henry's law constant
Soil-water partition coefficients
August 1995 5-138
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5.0 EXPOSURE S3. Concentrations for Human Receptors
• Organic carbon partitioning coefficient
• Dermal parameters.
Further details on the calculation of these properties are presented later in this section.
The approaches to determining a best estimate for those properties that were not
calculated varied somewhat depending on the variability of the property and the availability of
data. These approaches included use of default values, use of recommended values from
several comprehensive sources, and use of geometric mean of all plausible values located in
the literature. The choice of approach depended on the variability of the property, both
between chemicals and between different values for the same chemical; for example, a default
value approach was considered appropriate only for properties of little variability .between
chemicals, while a geometric mean approach was considered more appropriate for properties
with considerable variance between values for a specific chemical.
. Among the literature reviewed, the two best sources for chemical properties were The
Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic
Chemicals (Mackay et al., 1992 and 1993) and Handbook of Fate and Exposure Data
(Howard et al., 1990-1993); these are referred to throughout this section as Mackay et al. and
Howard et al. ,
Mackay et al. conducted a thorough review of available environmental handbooks, •
chemical reference books, and original literature papers in compiling chemical properties for
the Handbooks. Mackay et al. evaluated the compiled data and selected a "best" or
representative value for each property from among the values found. When selecting a
representative value, Mackay et al. considered age of the data, method of data determination,
the research objective, and die reported values for structurally similar compounds. Similarly,
Howard et al. conducted a literature review, evaluated the resultant data, and selected a "best"
value from among the values found. When a chemical was listed in either Mackay et al. or
Howard et al., the selected values in those references were used. Mackay et al. was preferred
over Howard et al. because it is more recent and it reviewed data from Howard et al.
Many HWIR chemicals were not in Mackay et al. or Howard et al., but properties of
these chemicals were available from a variety of other references. Where multiple literature
values were available, a geometric mean of the literature values was used; this was done
instead of selecting a single "best" value as Mackay et al. and Howard et al. did, in order to
avoid making subjective judgment ca&s. The types of literature reviewed included original
experiments, handbooks, and EPA repdrts and databases. '
Some references were used only .on a limited basis. The U.S. EPA ASTER (1992-
1993) database and the Superfund Public Health Evaluation Manual (SPHEM) (U.S. EPA,
1986d) were two such references. ASTER was used on a limited basis because much of the
data in ASTER were not referenced. Furthermore, the values in ASTER often differed
greatly from values in other, well-respected references. SPHEM was used on a limited basis
because much of the data in the report were compiled from unreliable sources.
August 1995 . 5-139
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5.0 EXPOSURE 5.2 Concentrations for Human Receptors
For each property that was not calculated, the approach used is identified below; for
more details on a. specific property, see the property-specific sections that follow.
Diffusivity in air-tends not to vary much even among chemicals. A single, fairly
comprehensive source for these values was available, the CHEMDAT7 database, and this was
used. If data for a specific chemical were not available, default values were used.
Vapor pressure values presented in the literature for a specific chemical can vary
considerably. If a recommended value was available in either Mackay et al. or Howard et al.,
that was used. Otherwise a geometric mean of literature values was used. If no value could
be found, the chemical was eliminated from the analysis.
Solubility values presented in the literature for a specific chemical tend not to vary
greatly, although there are significant differences in solubility between chemicals. If a
recommended value was available in either Mackay et al. or Howard et al., that was used.
Otherwise a geometric mean of literature values was used. If no value could be found, the
chemical was eliminated from the analysis.
Molecular weight values for a specific chemical do not vary. Therefore, once a value
was located in any source, it was used.
Kow values presented in the literature for a specific chemical can vary considerably.
Because many of the biotransfer factors are based on Kow, this is a critical parameter.
Therefore, as many values as possible were located in the literature for Kow, and a geometric
mean approach used.
Diffusivity in Air. The diffusivity in air data were obtained directly from the
CHEMDAT7 database. If constituents of interest were not found in this source, the standard
default values presented in this reference were assigned for these parameter values. These
parameters do not vary much, and the default values appear to be within the range of typical
values. Values for diffusivity in air range from about 0.01 to 0.1 cm2/s; the default value was
0.08 cm2/s.
Henry's Law Constant (H and H'). The Henry's law constant (H) is applicable only
to organic compounds and can be derived by a number of methods. It can be calculated from
the theoretical equation defining the constant, it can be measured, or it can be estimated from
the chemical structure. Because Henrys law constant can be difficult to measure accurately,
.values calculated from the theoretical equation are preferred to measured values. -Where
possible, Henry's law constant was calculated using the theoretical equation as presented in
Lyman et al. (1990): • . _ . .
(5-73)
August 1995 5-140
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5.0 EXPOSURE . 5.2 Concentrations for Human Receptors
where
H = Henry's law constant (atm-nvVmol)
VP - vapor pressure (atm)
MW = molecular weight (g/mol)
S = solubility (g/nr).
i
Vapor pressures and solubilities were taken from Mackay et al. or Howard et al. if
possible. A literature search for vapor pressure and solubility values was also conducted, and
the geometric mean of values found in the literature was calculated and used for chemicals
not reported in Mackay et al. or Howard et al. Vapor pressures from the EPA database
ASTER were used when no other data were available. The references from which vapor
pressure values were taken are listed in Table 6-21 in Section 6. Solubilities from U.S. EPA
(1985k) were used when no other data were available; however, many of these values are
calculated from the octanol-water partition coefficient (K^). If the KQW upon which the
solubility was based differed from the Kow selected for this analysis, that solubility value was
not used. For a few chemicals that are miscible with water, solubility data were not
available. Estimates of solubility for those chemicals were made using the Henry's law
constant and vapor pressure cited in Howard et al. (using Equation 5-73 solved for S). The
references from which solubility values were taken are listed in Table 6-22 in Section 6.
Vapor pressure and solubility were corrected to 25 °C before calculating Henry's law
constant
Molecular weights are constant for a specific compound and should not vary.
Therefore, geometric means were not used iSc molecular weight—a single value from any
source was considered acceptable. The references from-which molecular weight values were
taken are listed in Table 6-23 in Section 6.
A literature search for Henry's law constant was also conducted; if one of the inputs
needed to calculate Henry's law constant was not available, the geometric mean of measured
values from the literature was used. The references used for Henry's law constant are listed
in Table 6-24 in Section 6.
Some models require a dimensionless form of Henry's law constant, H', which is
calculated as follows: .
„. H' =JL. (5-74)
where
H' = dimensionless Henry's law constant (unitless)
H = Henry's law constant (atnvmVmol)
R = universal .gas constant = 8.205e-5 atm-m3/mol-K
T = temperature (K). •
August 1995 5-141
1
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5.0 EXPOSURE 5.2 Concentrations for Human Receptors
The temperature in the above equation should be the same as the temperature to which the
Henry's law constant (H) has been corrected; in this case, 25 °C.
Soil- Water Partition Coefficient (K,,). For organic compounds, past research has
demonstrated that for hydrophobic organic compounds, soil organic matter is the dominant
sorbing component in soil. Therefore, the soil-water partition coefficient, Kj, is calculated
from the organic carbon partition coefficient and the fraction of organic carbon in soil for the
organic constituents, as follows (Domenico and Schwartz, 1990):
where
Kd = soil- water 'partition coefficient (mL/g)
KOC = organic carbon partition coefficient (mL/g)
f^. = fraction organic carbon in soil (unitless).
The f^ term is a soil-specific parameter. See Section 6.8.3.1 for a discussion of how
values were selected.
An extensive literature search was conducted for K^ values; however, these values can
vary enormously for one chemical. The literature values obtained were not considered
sufficiently reliable and consistent for use in this analysis. Therefore, K^. is calculated from
the octanol-water partition coefficent (Kow) using two correlation equations. These correlation
equations are based on literature values of Rj,w and K^ for a variety of compounds for which
good data were available. The first applies to phthalates and polycyclic aromatic
hydrocarbons (PAHs):
log(A'oc)=0.971og(A:o(V) -0.094 . (5-76)
The second equation is more widely applicable and is used for all other organic compounds,
except 2,3,7,8rTCDDioxin.
log(AToc)=0.781og(A:w) -0.151 . (5-77)
For 2,3,7,8-TCDDioxin, a weighted value reflecting all congeners with nonzero toxicity
equivalence factors (TEFs) is used.
Log Kow values were chosen ojyi case-by-case basis from an extensive database
containing measured values from different analytical techniques (e.g., slow stir, shake flask,
generator column) and predicted values from two chemical structure models, SPARC and
CLOGP. Dr. Samuel Karickhoff of the U.S. EPA Environmental Research Laboratory in
Athens determined a recommended log Kow value for each chemical based on professional
judgment and general criteria for selecting log Kow values established in intra-Agency
. workgroup sessions. The consensus criteria were generally consistent with methods
developed for the Great Lakes. Water Quality Initiative Technical Support Document (U.S.
EPA, 1995a). Typically, the average of appropriate measured values was the basis for a
August 1995 5-142
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5.0 EXPOSURE 52 Concentrations for Human Receptors
recommended value. However, in some cases, Dr. Karickhoff recommended the SPARC,.
CLOGP, or average predicted value based on the compatibility of the model with the structure
of a particular chemical. Some of the primary references of log K,,w values included:
• Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for
Organic Chemicals—Volumes l-ttl (Mackay et al., 1992);
• Handbook of Environmental Fate and Exposure Data for Organic Chemicals
(Howard et al., 1990-1993); .
• • Derivation of Proposed Human Health and Wildlife Bioaccumulation Factors for the
Great Lakes Initiative (Stepban, 1993);
• Aqueous Solubility and n-OctanolfWater Partition Coefficient Correlations (Isnard
and Lambert, 1989); and
• Partition Coefficients (n-octanol-water) for Pesticides (Noble, 1993).
Bibliographic cross-referencing was conducted for all sources (journal articles and
compendia of log Kow values) and additional values were identified, often tracing back
through several "levels" of citations. In addition, on-line searches were conducted in (1)
National Library of Medicine databases such as Chemical Abstracts, TQXLINE, and HSDB,
(2) other databases available on the internet such as CARL UNCOVER, and (3) EPA
databases on predicted chemical properties s»rh as ASTER and CHEMFATE. Every effort
was made to identify and obtain the original sources for log Kow values. The references from
which the log Kow values were taken are listed in Table 6-25 in Section 6.
A more detailed discussion of the criteria used to select log KQW values is provided in
Section 6.8.6.1.3. These values and the recommended log Kow are summarized in the
•Internal Report on Summary of Measured, Calculated, and Recommended Log Kw Values
(Karickhoff and Long, 1995).
Unlike organic compounds in which Kj is largely controlled by the soil organic carbon
content, Kj values for inorganics are significantly affected by a variety of soil conditions.
The more important of these are pH, oxidation-reduction conditions, iron oxide content, soil
organic matter content, cation exchange capacity, and major ion chemistry. It is difficult to
derive generic Kj values for inorganics- because of the numerous significant influencing
parameters. The Kd values for inorganic constituents were generated by EPA using an
equilibrium geochemical speciation model (MINTEQA2).
August 1995 5-143
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5.0 EXPOSURE 5.2 Concentrations for Human Receptors
5.2.9.6.2 Chemical-Specific Dermal Parameters
The chemical-specific parameters that affect the dermal adsorption process of
contaminants were either taken from the Dermal document (U.S. EPA, 1992d) or calculated
from equations presented there.
The skin permeability constant in water, K_w, indicates the rate at which a contaminant
is transported across the skin while in an aqueous media. For organics, it is calculated from a
regression equation on the octanol-water partition coefficient and molecular weight, as
follows:
N
log/TDw = -2.72 +0.7liogATflM, -0.0061 MW (5-78)
r • W¥
where
x KpW = skin permeability constant in water (cm/h)
KQW "= octanol-water partition coefficient (unitless)
MW = molecular weight (g/mol).
For inorganics, KpW is set to 0.001 cm/h.
The Bunge constant, B, characterizes the effect of the viable epidermis on the
cumulative mass of contaminant that enters the stratum comeum, or outermost layer of the
skin. It is calculated as follows:
flg K*» (5-79)
10,000 •
where
B = Bunge constant (unitless)
Kow = octanol-water partition coefficient (unitless).
The Bunge constant is needed only for-organics.
The lag time, I, is a function of the skin stratum comeum diffusion coefficient and the
thickness of the human stratum comeum. The stratum comeum diffusion coefficient is
calculated as follows: -•»»
log
\
n
'sc
where
-2.72 -0.0061 MW
= stratum corneum diffusion coefficient (cm2/h)
= thickness of the stratum comeum = 0.001 cm.
August 1995 5-144
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5.0 EXPOSURE 5.2 Concentrations for Human Receptors
The lag time is then calculated as follows:
•'• *i ' '
t-^fd. (5-81)
where
t = lag time (h)
Dx = stratum comeum diffusion coefficient (cm2/h)
1K = thickness of the stratum comeum = 0.001 cm.
The lag time is needed only for organics.
The time required by a contaminant to reach a steady-state skin flux, t*, is a function of
the Bunge constant, B, and the lag time, T, and is calculated as follows.
For BS 0.1:
For 0.1 < B < 1.17:
f'=(8.4+61ogB)T (;
For B £ 1.17:
(5-84)
d«±(lffi)2-c (5-85)
7t
c=I^£ (5-86)
where
t* = time to reach steaay-state skin flux (h)
B = Bunge constant (unitless)
T = lag time (h). •
An absorption fraction was calculated using equations presented in the Dermal
document (U.S. EPA, 1992d) for most constituents (see Equation 5-13). However,
experimental values for the absorption fraction were available and recommended for use in
the Dermal document for dioxins and cadmium; therefore, these were used. Other
experimental values are presented in the Dermal document but are not recommended for use.
August 1995 5-145
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5.0 EXPOSURE 5.2 Concentrations for Human Receptors
Exposure studies must be evaluated carefully for their applicability to the types of exposure
scenarios being modeled before they can be used with confidence.
5.9.2.6.3 Health Benchmarks
* The health benchmarks used were taken from EPA's Integrated Risk Information
System (IRIS) whenever possible. If data were not available on IRIS, values from Health
Effects Assessment Summary Tables (HEAST) were used. If these were also not available,
other EPA documents were searched for appropriate values. The health benchmark data for
all HWIR constituents are summarized in Appendix A. Health benchmarks are discussed
more fully in Section 4.
5.2.10 Uncertainty .
/ •- '-'
The dermal exposure algorithms are quite new and are still experimental in nature.
Widespread application of these algorithms to many chemicals, as is done here, has never
been undertaken before. In particular, the skin permeability constants are highly uncertain.
Good experimental data on these exist for only a very few compounds, and the estimated
values used here are based on algorithms still undergoing peer review and development
However, these are the best algorithms currently available. A further uncertainty attendant on
the dermal route of exposure is the lack of dermal health benchmarks. Current practice is to
use oral health benchmarks; however, for many chemicals, there may be differences in
absorbed dose between the oral and dermal routes. Where such differences are known (i.e.,
for dioxins), they have been accounted for, but they are not known for most of the
constituents covered by this analysis.
August 1995 5-146
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5.0 EXPOSURE 5J Concentrations for Ecological Receptors
5.3 CONCENTRATIONS FOR ECOLOGICAL RECEPTORS
5.3.1 Conceptual Approach
The development of medium-specific concentrations for the protection of ecological
receptors was based on two key assumptions. First, that a significant exposure pathway for
ecological receptors was via the ingestion of contaminated vegetation or prey. Numerous
studies have demonstrated the capacity of hydrophobia organic chemicals to bioaccumulatc to
concentrations in the food chain that are orders of magnitude above the concentration in the
contaminated medium (e.g.. Oliver and Niimi, 1988). Second, receptors that live in intimate
contact with a contaminated medium (e.g., daphnids, plants, soil fauna) are more exposed by
virtue of their spatial relationship to the contaminant However, it is important to recognize
that food chain pathways are significant even for constituents that do not bioaccumulate
appreciably. Dietary exposure to constituents that concentrate weakly in fish tissue (e.g.,
bioconcentration factor below, 10) may be more significant than exposure to contaminated
drinking water simply because a particular animal ingests relatively more fish than water.
i . • • .
These assumptions have important implications for the development of "protective"
concentrations for ecological receptors. For constituents that bioaccumulate, particularly those
that biomagnify,* benchmarks should account for exposure through the ingestion of
contaminated prey as well as contact with or ingestion of a contaminated medium.
Unfortunately, the majority of lexicological studies provide effects levels for a single route of
exposure and seldom consider the potential increase in exposure concentrations for successive
trophic levels. For example, a chronic toxicity value for a piscivorous fish measured in a
typical flow-through assay (i.e., constant contaminant concentration in water) usually does not
include exposure through the ingestion of prey that live in the same contaminated water. As
a result, benchmarks for bioaccumulative constituents cannot be used directly; exposure
estimates must incorporate the bioaccumulation potential in the food chain. For nonbio-
accumulating constituents, no effects concentrations (NOECs) or other benchmarks that are in
units of concentration (i.e., mg/kg or mg/L) can be used directly as "protective"
concentrations for ecological receptors (e.g., Final Chronic Values)..
For receptors in the freshwater ecosystem, methods are presented for food chains
associated with limnetic and littoral ecosystems. Although protective exposure concentrations
are calculated for both types of freshwater ecosystems, acceptable waste concentrations were
only backcalculated for die littoral ecosystem. The flow chart in Figure 5-1 illustrates the
series of steps used to estimate surfa@&,water concentrations that should be protective of
aquatic wildlife for bioaccumulative and nonbioaccumulative chemicals. For bioaccumulative
chemicals (log K^ > 4), measured bioaccumulation factors (BAFs) were identified or
predicted using models developed by Thomann (1989) for the limnetic (or pelagic) food chain
and Thomann et al. (1992) for the littoral food chain (i.e., sediment-based). Second, the
•Biomagnification is defined as the tendency for the concentration of contaminants to increase with trophic
level.
August 1995 5-147
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5.0 EXPOSURE
SJV Concentrations for Ecological Receptors
Estimate Bioaccumulatlon Factors (BAFs), Biota-Sediment
Accumulation Factors and Bioconcentration Factors (BCFs)
Bloaecumulatlng Chemicals
,»LogKow>4
• Field BAF available
Nonbiooceumulating Chemical*
•LogKow<4
•NofiekJBAF
Predicted BAFs
•109^4-6.5
• Limnetic model
(Thomann. 1989)
• Littoral model
(Thomann et al,
1992)
Measured BAFs
•LogKow>6.5
• Metabolizable
•Metals
Measured BSAFs
.•TCDO
•PCBs
Predicted BCFs
• Equation specific
to chemical family
• Few measured
values
• No measured data
Measured BCFs
• Metabolizable
•Metals
• Geometric mean
of measured
values
Estimate Acceptable Tissue Concentration (TC) in Prey Items |
Based on
• Toxicotogical benchmarks
• Dietary preference
• Body weight, food intake
Calculate Protective Surface Water and Sediment Concentrations
Based on
• Tissue concentration
• BAF, BSAF, or BCF for appropriate trophic level
> Includes benchmark for sediment
community using EqP rfftthods
> Includes ingestion
of surface water
Figure 5-1. Schematic of steps needed to calculate protective exposure
concentrations in the freshwater ecosystem.
August 1995
5-148
-------
5.0 EXPOSURE 5.3 Concentrations for Ecological Receptors
acceptable tissue concentration (TC) was estimated for prey based on the intake, body weight,
and dietary preference (i.e., trophic level of fish consumed) of the representative predator
species. Last, the protective surface water concentration was calculated by dividing the tissue
concentration (TC) by the bioaccumulation factor for the appropriate trophic level. The
approach and algorithms used to estimate BAFs for the two freshwater ecosystems are
presented so that all inputs and assumptions can be evaluated. For nonbioaccumulative
chemicals, the protective surface water concentration for fish and aquatic organisms was the
Final Chronic Value (FCV) or Secondary Chronic Value (SCV) as described in Section 4.
For upper trophic level aquatic wildlife such as mink and osprey, protective surface water
concentrations were calculated based on the consumption of contaminated fish and water.
The oentnic community was included in the littoral ecosystem. Protective sediment
concentrations were estimated using the equilibrium partitioning (Eqp) methods developed by
Di Toro et al. (1991). As explained in Section 4, the sediment benchmark was calculated by
multiplying the FCV (or SCV) by the octanol/carbon partition coefficient (K^) and adjusting
for the fraction organic carbon (f^ in the sediment
For receptors in the generic terrestrial ecosystem, methods' are presented for wildlife
representing a range of dietary habits across trophic levels, including mobile organisms that
live in the soil (i.e., soil fauna). Figure 5-2 illustrates the steps followed to estimate
protective soil concentrations for each group of ecological receptors of concern (e.g., plants,
mammals, soil fauna). For plants, the lowest NOEC soil concentration identified in Suter and
Mabrey (1994) was used to backcalculate to acceptable waste concentrations. For soil fauna,
a statistical approach was used to determine a soil concentration protective of 95 percent or
the soil species at the 50 percent confidence Jevel. For higher trophic level wildlife, dietary
preferences, daily intake, and bioconcentration factors for prey items were identified or
estimated to calculate protective soil concentrations. The key equation used to backcalculate
soil concentrations as a function of dietary exposure (including soil ingestion) was developed
for the Agency's assessment of risks to terrestrial wildlife from TCDD and TCDF in pulp and
paper sludge (Abt Associates, Inc., 1993). The exposure inputs (e.g., body weights, daily
intake) are presented for ecological receptors, as appropriate, and the primary sources for
these values are discussed. The following sections describe the methods used to backcalculate
protective exposure concentrations for the ecological receptors in the generic freshwater and
terrestrial ecosystems.
5.3.2 Generic Freshwater Ecosystem
A key element in backcalculatigg protective exposure concentrations in the freshwater
ecosystem was the development of values for bioaccumulation and bioconcentration.*
Constituents of concern were divided into two broad categories: .bioaccumulative constituents
and nonbioaccumulative constituents. As defined above, bioaccumulative constituents were
•Bioaccuraulatiqn is defined as the concentration of a chemical .in the tissues of an organism as the result of
direct contact and ingestion of contaminated prey or vegetation. Bioconcentration is defined as the concentration
of a chemical in the tissues of an organism as the result of direct contact with a contaminated medium.
August 1995 . 5-149
-------
5.0 EXPOSURE
5J Concentrations for Ecological Receptors
Estimate Bloaccumulatlon Factors (BAFs) and Bloconcentratlon
Factors (BCFs) for Terrestrial Food Items
J
Four -Plants
Categories • Earthworms
• Other invertebrates
•Vertebrates
Predicted Values
• Hydrophobia organics
• Based on lipid partitioning theory
• TCDD used as standard
Measured Values
• Always preferred
• Required for metals, polar organics
• Geometric mean for species
in same category
Calculate Protective Soil and Plant Concentrations
J
Based on
• BAFs and BCFs for food items
• Dietary preference
• Body weight, food intake
Protective Soil Concentrations
for Omnivores and Carnivores
Protective Plant Concentrations
for Herbivores
Includes benchmark for soil community
Includes ingestion of soil
Includes benchmark for plants
Figure 5-2. Schematic of steps needed to calculate exposure
concentrations in the terrestrial ecosystem.
August 1995
5-150
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
defined as constituents with either (1) log Kow values above 4 or (2) empirical evidence for
bioaccumulation -nrthe field (e.g., lindane). Bioaccumulation factors for large fish (assumed
trophic level 4) and small fish (assumed trophic level 3) were generally estimated for
chemicals with log Kow < 6.5 using models developed by Thomann (1989) for the limnetic
ecosystem and Thomann et al. (1992) for the littoral ecosytem. The underlying principle
behind the Thomann bioaccumulation models (and most bioaccumulation theory) is that, at
steady-state equilibrium, bioaccumulation of hydrophobic (i.e., lipophilic) organic constituents
occurs in the Upid tissues of aquatic organisms. This principle is a simplification of a more
complex process of bioaccumulation since (1) accumulation, even for hydrophobic organics, is
not limited exclusively to lipid, and (2) processes such as growth and metabolism may not be
sufficiently accounted for. Nevertheless, it is a useful assumption in terms of biological
uptake modeling and is reasonably accurate within the bounds of biological variability
(personal communication with Russell. Erickson of EPA Environmental Research Laboratory,
Duluth, March 25, 1994). This assumption has important implications for bioaccumulation
modeling since the potential of a chemical to accumulate in lipids has been correlated with its
log octanol/water partition coefficient (log K^). A number of authors have demonstrated that
food chain accumulation tends to increase with increasing log Kow, and Thomann (1989)
estimates that at log Kow of - 6.5 or above, food chain accumulation accounts for virtually
the entire concentration in the top predators in freshwater ecosystems. The mechanism or
driving force behind the biomagnification of hydrophobic organic chemicals in the food chain
has been shown to be the increase in chemical fugacity of contaminated food being digested
in the gastrointestinal tract of fish (Gobas et al., 1993a, b).
In addition to the Thomann models, th? BAFs selected for the exposure calculations
were sometimes measured values as cited in Stephan (1993) or default values selected for
certain groups of constituents. Because the models were developed for hydrophobic organics
with log Kow values below - 6.5 and assume negligible metabolism, only measured values
were used for the "superlipophilic" constituents (i.e., log Kow > 6.5), constituents believed to
be metabolized, and for metals. For example, polycyclic aromatic hydrocarbons (PAHs) have
been shown to be metabolized in a variety of fish species and, consequently, measured BCF
and BAF values are orders of magnitude below predicted values. Therefore, measured
BCF/BAF values were used whenever possible for PAHs and, when unavailable, a default
lipid-based BAF of 1,000 was used based on recommendations by Stephan (1993) and an
analysis of available data on accumulation of PAHs. For 2,3,7,8-TCDD, biota sediment
accumulation factors (BSAFs) were used to estimate protective sediment concentrations
instead of surface water concentrations. Recent reports produced by EPA such as the Interim
Report on Data and Methods for Assessment of2J,7,8-Tetrachlorodibenzo-p-dioxin Risks to
Aquatic Life and Associated Wildlife (U.S. EPA, 1993i) suggest that the BSAF is a more
reliable measure of bioaccumulation potential due to the analytical difficulties in measuring
dissolved concentrations in surface water.
For both bioaccumulative and nonbioaccumulative constituents, fish bioconcentration
factors (BCFs) were (1) identified in the open literature or Agency references (e.g., Stephan,
1993), (2) calculated based, on the relationship between log Kow and bioconcentration in
lipids, or (3) estimated using empirical regression equations (e.g., Veith et al., 1979).
August 1995 5-151
-------
5.0 EXPOSURE 5.3 Concentrations for Ecological Receptors
Bioconcentration factors estimated with the Thomann equation were relevant to the freely
dissolved concentration in water (as opposed to the total water concentration). For
nonbioaccumulative constituents (i.e., log Kow < 4), bioconcentration factors in fish were
assumed to refer to dissolved water concentrations (i.e., dissolved water concentration is equal
to total water concentration). This is a reasonable assumption for chemicals with log K^
values below 4; however, it is not valid for more hydrophobic chemicals because they tend to
sorb to organic matter in surface water and are found in high concentrations in the sediment
On a case-by-case basis, bioconcentration factors were identified or estimated for constituents
of concern. The geometric mean of measured values (normalized for lipid content for
hydrophobic organics) was compared to predicted values for organic chemicals with a log
KOW value below 6.5. If the values did not differ appreciably (i.e., more than a factor of 4)
and the chemical was considered not to be metabolized, the predicted BCF was typically .
used. The criterion for appreciable differences between predicted vs. measured BCFs was
based on a study by Randall et al. (1991) in which the authors demonstrated variation in BCF
values between a factor of 2 and 4 based on lipid extraction techniques alone. For
hydrophobic organic chemicals with a log Kow value above 6.5 and metabolizable chemicals
(e.g., PAHs), the geometric mean of lipid-based measured values was used. For metals, the
geometric mean of whole-body BCF values was calculated for use in the exposure
calculations.
To account for food chain exposures, a three-part methodology was developed to
backcalculate protective exposure concentrations in surface water
1. Estimate or identify lipid-based biocon/^ntration (BCFf) and bioaccumulation factors
(BAFI) for each constituent, as appropriate, for fish and aquatic invertebrates.
2. Based on the benchmark concentration identified for fish (e.g., FCV, SCV), determine
the lipid-based tissue concentration (TCf) for:
• Fish ingestion by piscivorous mammalian species
• Fish ingestion by piscivorous avian species.
3. For bioaccumulative or bioconcentratable contaminants, estimate the protective exposure
concentration by dividing the lipid-based TQ by the lipid-based bioconcentration or
bioaccumulation factor for the trophic level from which prey were consumed (mammals
and birds) or (2) dividing the lipid-based TCC by the lipid-based bioaccumulation factor
(fish). For nonbioaccumulative*€ontaminants, the protective exposure concentrations for
fish/aquatic invertebrates and benthos were the same as the benchmark concentrations
presented in Section 4.
The protective exposure concentrations for birds, mammals, and fish via the food chain
are shown in Tables 5-15 and 5-16 for the limnetic and littoral ecosystems, respectively.
. Each table presents a matrix of ecological receptors, constituents of concern, and protective
surface water and sediment (littoral) concentrations. The tables also indicate whether the
surface water values represent dissolved or total water column values. The protective surface
August 1995 5-152
-------
Table 5-15.
Constituent name
Acenaphthene
Aldrin
Antimony
Arsenic
Benz(a)anthracene ( 1 ,2-)
Benzo(a)pyrene
Bis(2-ethylhexyl)phthalate (also|
DEHP)
Butylbenzyl phthalate
Cadmium
Chkudane
Chromium VI
Chrysene
Copper
DDT
Di-n-octyl phthalate
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Endosulfan
Endrin
Fluoranthene
Protective
Dissolved or
total
d
d
d
d
t
t
d
d
d
d
d
t
d
d
d
d
d
d
d
d
l
Exposure Concentrations for
(all units in
Lipid-based
or whole-
body
lipid
lipid
whole '
whole
lipid
lipid
lipid
lipid
whole
lipid
whole
lipid
whole
lipid
lipid
lipid
lipid
lipid
lipid
lipid ;
lipid
BCF or
BAF Mink
BCF ED
BAF 2.2e-07
BCF 1.6e+00
BCF 5.5e+00
BAF ED
BAF 2.8e-02
BAF 8.4e-01
BAF 3.7e-01
BCF 2.7e-02
BAF 2.4e-03
BCF 4.9e+00
BAF ED
BCF 2,3e+0l
BAF 4.3e-07
BAF ED
BAF l.Oe-06
BCF ED •
BCF ED
BCF 6.U-02
BAF 5.0e-05
BAF ED
Ecological Receptors in
mg/L)
River
otter
ED
8.4e-08
8.6e-01
3.3e+00
ED
9.4e-03
2.7e-01
1.2e-01
1.7e-02
7.7e^06
3.1e+00
ED
1.2e+01
8.3e-08
ED
3.6e-07
ED
ED
3.8e-02
1.7e-05
ED
Bald
eagle
ED
1.2e-08
ED
7.6e+00
ED
ED
ED
ED
6.2e-02
ED
ED
ED
ED
1.7e-08
ED
4.4e-06
ED
ED
ED
5.2e-06
ED
Osprey
ED
1.6e-08
ID
5.6e^00
ED
to
ED
ED
4.3e-02
ED
ED
ED
ED
1.8e-08
ED
3.1e-06
ED
ED
ED
4.9e-06
ED
the Limnetic Ecosystem
Great
blue
heron
ED
9.6e-09
ED
6.1eiOO
ED
ED
ED
ED
4.7e-02
ED
ED
ED
ED
l.le-08
ED
2.9e-06
ED
ED
.ED
3.5e-06
ED
Kinfisher
ID
1.2e-08
ED
4.4e+00
ED
ED
ED
ED
3.4e-02
ED
ED
ED
ED
1.5e-08
ED
2.6e-06
ED
ED
ED
3.4c-06
ED
Herring
gull
ED
2.1C-08
ED
6.9e+00
ED
ED
ED
ED
5.2e-02
ED
ED
ED
ED
1.9e-08
ED
4.1e-O6
ED
ED
ED
5.3e-06
ED
Fish
(food chain)
'(2.3e-02
1.9e-07
3.0e-02
8.1e-03
1.3e-03
1.3e-05
5.4e+00
1.3e+01
l.le-03
4.5e-06
l.le-02
ED
1.2c-02
5.6e-08
ED
1.2e-05
2.2e-01
1.4e-fO2
5.6e-05
2.0e-05
6.2e-03
• . (continued)
5.0 EXPOSURE 5.3 Concentrations for Ecological Receptors
-------
Constituent name
Hexachlorobenzene
Hexachlorocyclohexane, gamma-
(Lindane)
Hexachlorocyclopentadiene •
Hexachlorophene
Kepone
Lead
Mercury ^
Methyl paraihion
Molybdenum
Nickel
Paraihion .
Pentachlorobenzene .
Pentachlbrophenol
Polychlorjnated biphenyls
(Aroclor-1254)
. Selenium
TCDD, 2;3.7,8-
Toxaphene
Trichlorophenoxy acetic acid, 2,4,5-
Zinc
-
Lipld-bascd
Dissolved or or whole- .
total body
t
t
d
-
d
t
I
d
t
d
d
d
t
d
d
d
I
d
t
lipid
lipid
lipid
lipid
lipid
whole
whole
lipid
whole .
whole
lipid
lipid
lipid
lipid
whole
lipid
lipid
lipid
whole
Table 5-15
BCF or
BAF Mink
BAF 8.3e-OS
BAF ED
BAF 6.1e-fOO
BAF ED
BAF Lie 03
BCF 4.5e-04
BAF 6.7e-05
BCF 1.5e-01
BCF ID
BCF i-4e+02
BCF 4.3e-03
BAF l.le-03
BAF 1.6e-02
BAF 8.9e-07
BCF 2.1e-03
BAF 1.7e-12
BAF 1.8e-06
BCF 1.3e+OO
BAF 5.8e-K»
(continued)
River
otter
2.3e-05
ED
2.1e+00
ED
3.8e-04
1.3e-03
5.7e-06
9.U-02
ID
8.3e+Ol
2.6e-03
3.8e-04
5.2e-03
1.7e-07
1.4e-03
6.0e-13
5.9e-07
7.2e-01
1.9e+00
ED = Insufficient data.
Note: The protective exposure concentrations (in mg/L) for aquatic plants and fish/aquatic invertebrates
exposure concentration for the sediment community (in mg/kg sediment).
Bald
eagk
1.8e-05
1.4e-03
ED
ED
9.4e-03
l.le-03
3.0e-07
4.0e-02
ED
ED
1.6e-02
ED
l.le-02
4.4e-07
6.9e-02
L5e-10
5.9e-08
ED
CD
Osprey
1.7e-05
9.9e-04
ED
ED
6.7e-03
8.1e-04
l.Oe-06 •
2.8e-02
ED
ED
l.le-02
ED
1.6e-01
6.8e-07
4.9e-02
3.6e-ll
3.5e4»
ED
ED
Great
blue
heron
1.3e-OS
l.le-03
ED
ED
7.1e-03
8.9e-04
2.0e-07
3.0e-02
ED
ED
L2e-02
ED
1.6e-01
3.7e-07
5.2e-02
7.8e-ll
3.9e-08
ED
ED
Kinfbher
1.3e-05
7.8e-04
ED
ED
5.1e-03
6.3e-04
7.9e-07
2.2e-02
ED
ED
8.8e-03
ED
L2e-01
5.2e-07
3.7e-02
2.8e-ll
19e-09
ED
CD
Herring
gull
2.0e-05
1.2e-03
t
ED '
ED
8.2e-03
l.Oe-03
1.3e-06
3.3e-02
ED
ED
L4e-02
ED
1.9e-01
8.2e-07
5.8e-02
4.6e-ll
5.0e-08
ED
ED
(direct contact) are presented in Table 4-4 along -with the
Fish
(food chain)
1.7e-03
' L2e-oll
7.5e-04
ED
2.9C-04
3.2e-03 .
Lle-04
3.2e-05
2.4e-01
1.6e-01
1.3e-05
1.3e+OO
4.0e-03
3.6e-07
S.Oe-03
ED
1.3e-07
l.Oe-02
L2e-01
protective
in
b
Pi
X
i
c
o
ca
f
0
C.
I
93
8
•o
0
-------
Table
Conathuont name
Acenaphlhene
AWrin
Antimony
Arsenic
Barium
Benz(a)anihracene (1.2-)
Benzo(a)pyrene
Beryllium
Bis(2-6lhylhexyl)phlhalaie (also
DEHP)
Butylbenzyl phthalate
Cadmium
Chtordane
Chromium VI
Chryten*
Copper
DDT
Dkvoctyl ptnhdata
DWdrin
Diothyl phihalate
Dimalhyl prnhatota
EndoMittan
Endrin
Fkjoranihen*
HeptacNor
Heptachlor epoxide
5-16.
DtesoJtfe
or Mai
. d
d
i
. t
t
t
i
at
' '
d
t
d
t
t
t
d
t
d
d
d
d
d
t
d
d
Protective
UpkMwaed
body
ipid
Ipid
whole
whole
whole
Ipid
Ipid
whole
ipid
Kpid
whole
ipid
whole
IpU
whole
IpU .
IpU
IpU .
IpU
IpU
IpU
IpU
IpU
IpU
IpU
Exposure Concentrations for Ecological Receptors in
(all units in mg/L except for TCDD8)
BCF.
BAF, or
B8AF
BCF
BAF
BCF
BCF
BCF
BAF
BAF
BCF
BAF
BAF
BCF
BAF
BCF
BAF
BCF
BAF
BAF
BAF
BCF
BCF
BCF
BAF
BAF
BAF
BAF
River
Mink otter
ID ID
8.46-08 328-08
1.66+00 8.66-01
5.50+00 3.30+00
ID ID
ID ID
3.46-02 1.26-02
ID ID
1.16+00 3.46-01
4.66-01 1.56-01
2.76-02 1.76-02
1.76-05 5.36-06
4.96+OO 3.10+00
ID ID
230+01 126+01
5.4047 1.06-07
. ID ID
1.16-06 3.80-07
. ID ID
ID ID
7.6o42 2.8*42
5.0*45 1.8*45
ID ID
1.6*44 6.0045
ID ID
Groat
Bald blue
eegle Oaprey heron Mallard
ID ID ID ID
4,0649 6.28-09 3.36-09 1.4648
ID ID ID ID
7.66+00 .5.68+00 6.16+00 ID
1.16+03 9.98+02 1.16+03 1.06+03
ID ID ID ID
ID ID ID ID
ID ID ID ID
ID ID . ID ID
ID ID ID ID
' 626-02 4.36-02 4,76-02 ID
ID ID ID ID
ID ID ID ID
ID ID ID ID
ID ID ID ID
8.3648 22048 5.6648 ID
ID ID ID ID
23046 32*48 1.6*46 4.6*46
ID ID ID ID
ID ID ID ID
ID ID ID ID
2-8*48 6.5*48 1.9*46 5.6*46
ID I'D ID ID
ID ID ID ID
ID ID ID ID
teaiar
Scaup
ID
1.28-08
ID
ID
1.06+03
ID
ID
ID
ID
ID
ID .
ID
ID
ID
ID
ID
ID
3.9*48
ID
ID
ID
4.8*48
ID
ID
ID
the Littoral Ecosystem
Klng-
•ahor
ID
4.7648
ID
4.46*00
8.76+02
ID
ID
ID
ID
ID
3.4642
ID-
ID
ID
ID
1.9*48
ID
2.7*46
ID
ID
ID
3.9*48
ID
ID
ID
Spotted Spotted
•and- aandplpor
piper (aodlmont)
ID ID
1.26-08 7.46-02
ID ID
ID 7.00+01
7.86+02 7.3e+02
ID ID
ID ID
ID ID
ID ID
ID ID
ID 2.50+01
ID ID
ID ID
ID ID
ID ID
ID 2.9041
ID ID
-4.0*46 8.3*41
ID ID
ID ID
ID ID
4.4*46 3.4*41 .
ID ID
ID ID
ID ID
guH .
ID
7,.9e-09
...i
. ID
6.86+00
1.16+03
ID
ID
ID
ID
ID
526-02
ID
ID
ID
ID
2.4*08
ID
43*48
ID
ID
ID
8.0*06
ID
ID
ID
Ftah
(food chain)
I 23*42
1 1.36-07
3.06-02
8.1643
1.06+00
1.36-03
1.36-05
5.1643
546+00
1.48+01
1.1643
4.36-06
1.16-02
ID
1.2e42
5.6046
ID
12045
22041
1.40+02
5.6045
22045
62043
3.7043
3.8o41
(continued)
5.0 EXPOSURE 5 J Concentrations for Ecological Receptors
-------
.
ConetHuent name
Hexacrrioroberuene
Hexachbrccyctohexane.
gamma- (Undane)
Hexachlorocyclopentadiene
Hexachlorophane
Kepone
Lead
Mercury "
Methoxychlor
Methyl parathion
Molybdenum
Nickel
Parathion . .
Pentachtorobaruene
Pentachbrophenol
Polychlorlnated biphenyli
(Arodor-1254)
Selenium
Silver
TCDD. 2^.7.8-
Toxaphene
TArJiLuymluiumumjJia mrM
2.4.5-
Vanadium
Znc
Oieaolvec
er total
d
d
t
t
d
t
t
d
*$
t
I
d
d .
t
d
I
t
d
. t
d
t
. t
ID • InsufRcient ue$a.
* TCDD concernraSont In mg/Kg sediment.
Note: The protective exposure concentratkx
sediment community (In mo/kg eedkn
Llpld-baeed
or whole-
body
IpU
Ipid
lipid
Ipid
Ipid
whole
whole
lipid
lipid
whole
whole
Ipid
. IP*.
lipid
Ipid
whole
whole
Ipid
lpid
Ipid
whole
whole
BCF.
BAF. or
BSAF
BAF
BAF
BCF
BAF
BAF
BCF
BAF
BAF
BCF
BCF
BCF
BCF
BAF
BAF
BAF
BCF
BCF
BSAF
BAF
BCF
BCF
BCF
is (In ma/L) for aquatic plants
ent).
Table
River
Mink oder
8.18-05 2.88-05
ID ID
7.58+00 2.58+00
1.7e+01 9.38+00
1.38-03 4.80-04
4.58-04 1.38-03
6.78-05 5.78-08
1.58-01 ' 4.98-02
1.98-01 6.58-02
9.58-01 5.18-01
1.48+02 8.38+01
5.38-03 1.98-03
1.38-03 4.5e-04
3.88+01 2.28+01
4.1e-07 1.4e-07
2.1e-O3 1.4e-03
ID ID
2.5e-OS 8.7e-08
2.3e-Q6 ' 7.4e-07
1 .68+00 5.1e-01
4.48+OO 2.7e+00
5.80+00 3.38+00
5-16 (continued)
Bald
eagle Oeprey
9.58-06 1.6e-05
69e-O4 1.20-O3
ID ID
ID ID
518-O3 8.48-03
1.1803 8.18-04
3.08-07 1.08-06
ID ID
2.08-02 3.58-02
ID 10
ID ' t 10
v 88-03 1.4e-02
ID ID
4.48+01 7.9e+02
1.7e-07 3.1e-07
6.9e-02 4.9e-02
ID ID
4.4e-04 55e-04
3.08-08 4.48-08
ID ID
ID ID
ID ID
Great
blue
n*Mon Mvlwfd
7.08-06 2.18-05
5.58-04 ID
ID ID
ID ID
3.88-03 9.28-03
8.9e-04 ID
2.08-07 ID
ID ID
1. Se-02 .ID
10 ID
ID 10
6.28-03 ID
ID ID
828402 7.88+02
1.4e-07 6.8e-07
5.2e-02 ID
ID ID
2.3e-04 1.8e-04
2.0e-08 ID
ID ID
ID ID
ID ID
Leeaer
Scaup
2.1e-OS
ID
ID
ID
8.68-03
ID
ID
ID
ID
ID
ID
ID
ID
7.88+02
6.1e-07
ID
ID
1.8e-04
ID
ID
ID
ID
Klno-
•sher
1.2e-05
9.7e-04
ID
ID
6.48-O3
6.38-04
7.98-07
ID
27802
ID
ID
1.1e-02
ID
6.7e+O2
2.4e-07
3.7e-02
ID
4.1e-04
3.70-08
ID
ID '
ID
Spotted Spotted
sand- eandplper Herring
piper (sediment) guH
2.0e-05 4.58+00 1.98-O5
ID 9.78+00 1.68-03 |
• • " 1
ID ID ID
ID ID ID
8.18-03 6.98+01 1.06-02
10 1.18-01 1 .08-03
ID 8.68-02 1.38-06
- ID ID ID
ID 6.18+00 4.2e-02
ID ID ID
ID ID ID
ID 2.28+01 . 1.88-02
ID ID ID
6.1e+02 5.80+02 7.9e+02
5.88-07 2.20+00 3.88-07
ID 1.30+01 S.88-02
ID ID ID
i.4e-04 i.7e-04 6.60-04
ID 4.6e-01 6.3e-08
ID ID (D
ID I.Se+01 ID
ID ID ID
Ftoh
(lOOQ CIMHl)
1.88-03
1.28-05
7.58-04
ID
3.18-04
• 3.28-03
1.18-04
2:58-05
3.28-05
2.48-01
1.68-01
1.38-05
1.58+00
ID
2.88-07
5.08-03
3.68-04
ID
1.38-07
1.08-02
1.98-02
1 18-01
and flan/aquatic, invertebrates (direct contact) are presented In Table 4-4 along with the protective exposure concentration for the
tn
0
w
CA
O
O
s
1'
1
S
5"
t
90
1
0
-------
5.0 EXPOSURE 5.3 Concentrations for Ecological Receptors
water concentrations for fish/aquatic invertebrates and aquatic plants exposed via .direct
contact are presented in Table 4-4 along with protective sediment concentrations for benthos.
Although protective exposure concentrations are presented for both the limnetic and littoral
ecosystems, it should be noted that ecological exit criteria were backcalculated only for the
littoral ecosystem. Preliminary analyses suggested that the littoral ecosystem provided a more
realistic and conservative exposure scenario for ecological receptors associated with
freshwater ecosystems.
5.3.2.1 Limnetic Ecosystem "
5.3.2.1.1 Estimation of Bioaccumulation Factors
The limnetic zone was represented by a limnetic food chain that describes fish and
plankton typically associated with deep lake waters containing an abundance of plankton.
The highest trophic level fish are assumed to feed primarily on smaller, lower trophic level
fish, and lower trophic level fish are assumed to feed primarily on zooplankton and are
termed pianktivores. Thus, exposure in aquatic organisms (i.e., fish, daphnids) can occur
through the food chain or through direct contact with contaminated surface water. The model
used to estimate BAFs for hydrophobia chemicals consisted of a simple food chain:
phytoplankton-zooplankton-smali fish-large fish (Thomann, 1989).
Thomann (1989) defines the BAR* as
BAFlLlkglp*a°rganism ' (5-87)
Cd water
where Cl organism is the lipid-based concentration resulting from exposure to contaminated
water and food sources and C* water is the dissolved chemical concentration in surface water.
'*>» • -
For the simple four-level food chain used for the model, the solution to each level of
the food chain at steady state holds that the concentration in the lipid tissue of the organism is
equal to the lipid-based bioaccumulation factor times the dissolved concentration of the
chemical in water. Mathematically, this is given by
*Bioaccumulation factors (BAFs) estimated using the Thomann (1989) model reflect dissolved water
concentrations of hydrophobia organics. .
August 1995 . 5-157
-------
5.0 EXPOSURE
5 J Concentrations for Ecological Receptors
Cl organism = BAFt • C d water .
(5-88)
For organisms in the ith trophic level, the concentration in the lipid tissue is a function of the
uptake through direct contact across the gill membranes and the uptake through the
gastrointestinal tract following the consumption of contaminated prey. Accounting for factors
that affect the actual transfer of chemical from one trophic level to die next (i.e.,
bioaccumulation), Equation 5-88 may be rewritten as
Cl organism i*BCF1ixCd water+ (fi, i - 1 x C organising-1) (5"89)
t
where fi, i-1 represents the transfer of chemical from one trophic level to die next and is
defined as .
fi, i - .1 = ou, / - 1 [//, / - !/(«.+ GO] '• C5-90)
where
cu
li
Cf organism, i-1
Ki
Gi
chemical assimilation efficiency of organism i
food intake for the ith trophic level of food from the i-1 trophic level
lipid-based concentration in the i-1 trophic level
excretion rate, which accounts for elimination
growth rate, which reflects "growth dilution."
Thus, the concentration in the predator due to food intake would be expected to be greater
than that in prey under low Ki (excretion) and high (chemical assimilation efficiency).
Figure 5-3 presents die simple limnetic food chain used to estimate bioaccumulation
factors for each trophic level. Combining Equations 5-88 and 5-89 and rewriting to solve for
Level #1
Phyto-
plankton
^-
Level #2
Zoo-
plankton
»-
Level #3
Small
Fish
^
Level #4
Top
Predator
Dissolved Water Concentration
Figure 5*3. Simple limnetic food chain.
August 1995
5-158
-------
5.0 EXPOSURE 5 J Concentrations for Ecological Receptors
the lipid-bascd BAFfi for each trophic level results in the following three solutions for
trophic levels 2 through 3, respectively: .
(5-91)
(5-92)
(5-93)
where the transfer of chemical between trophic levels is defined as "fi, i-1" and accounts for
the transfer of chemical via intake of contaminated prey. Since the key to BAFfi is the
derivation of fi,i-l, the terms a, Ki, Gi, li and BCFfi are examined briefly below (see
Thomann, 1989, for a more complete discussion of these parameters).
Assimilation Efficiency (a). The chemical assimilation efficiency was approximated
from empirical data as the transfer efficiency of chemical across the gill, or E. For organisms
typically less than 10 to 100 g, a defined as E is given by:
log E =-2.6 + 0.5 log Kow for log K^, = 2 to <5
E = 0.8 for log Kow = 5 > to <6 (5-94)
log E = 2.9 - 0.5 log K,,w for log K^ * 6 > to 10 .
For organisms typically greater than 10 to 100 g, a defined as E is given by:
log E = -1.5 + 0.4 log K^ for log K^ = 2 to <3
E = 0.5 for log Kow = 3 > to <6 (5-95)
log E = 1.2 - 0.25 log Kow for log Kow = 6 > to 10 . '
From these equations, it is apparent that the efficiency increases with increasing log
Kow to a plateau, with a "subsequent decrease at higher log K,,w (>6-7).
Excretion Rate (Ki). The excretion rate is calculated as a function of uptake rate (ku)
and Kow, or
August 1995 5-159
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
with ku defined as:
ku = 1,000 (bw •%)£ <
where
bw = organism weight
y = respiration coefficient (from Q.2 to 0.3 for routine metabolism)
p = lipid fraction of the organism
E = empirically derived gill uptake efficiency (defined above).
Growth rate (Gi). The growth rate across a general food chain is approximated by
where 5 is -0.002 at 10 °C and the growth coefficient P varies between 0.2 and 0.3.
Intake (li). The intake, or food consumption, is estimated as a function of the growth
rate and respiration rate:
With the growth rate as defined above, a is the food assimilation efficiency (set at 0.8), and
the respiration rate (r) is given by
where $ varies between 0.014 and 0.05, with y described above for uptake rate.
Lipid-based Bionconcentration Factor (BCFI). Although the BCFI at steady state
may be derived from laboratory data, the BCFI for each trophic level was estimated for the
generic food chain by
. (5-101)
Ki
or, equivalently, as
BCFI* Kow 1
IQ^Kow
E(Kow)
l-i
(5-102)
However, Thomann (1989) points out that this equation is appropriate for a limited range of
Kow values. At log Kow above ~6, measured BCFI values tend to decrease due to the
decreased transfer efficiency of larger, high-molecular-weight chemicals across cell
membranes and the increasing effect of growth dilution. Because equilibrium partitioning
into the lipid pool is complicated by these factors, using log Kow as the BCFI for highly
hydrophobic chemicals may result in an overestimation of the true BCFI.
August 1995 5-160
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
Table 5-17 presents the input parameters used to calculate the lipid-based
bioaccumulation factors for the limnetic food chain consisting of phytoplanktor
zooplankton — small fish — large fish. With the exception of the values for chemical
assimilation efficiency, these input parameters are identical to the parameters used to derive
the food chain multiplier in the Wildlife Criteria Portions of the Proposed Water Quality
Guidance for the Great Lakes System (U.S. EPA, 1993h) and in the Assessment and Control
of Bioconcentratable Contaminants in Surface Waters (U.S. EPA, 1991g). The BCFfc and
BAFfs for trophic levels 3 and 4 are presented in Table 5-18 for each constituent The table
also indicates whether the value is based on dissolved or total water concentrations and
identifies them as predicted or measured.
5.3.2.1.2 Calculation of Tissue Concentration (TC)
The lexicological benchmark for fish was converted to the lipid-based tissue concentra-
tion (TCI in mg/kg Ip) assuming steady-state equilibrium. The TCI was calculated by
multiplying the lipid-based bioconcentration factor (BCFI in L/kg Ip) by the toxicity value
(e.g., FCV in mg/L) based on the equilibrium relationship between BCFI, and the ratio of
lipid-based concentration in the organism (Cl organism) and water concentration (C1 water)
by Thomann (1989):
BCFI L/kglp* Ct or*anism - Wffiglp . (5.103)
C d water mg/L
Setting Cd water equal to the fish benchmark value allows the equation to be solved for the
Cl organism, the lipid-based TCI. This equation is also valid for total water concentrations
as long as the bioconcentration factor and benchmark reflect a total water concentration.
Rearranging to solve for the Cl organism gives:
M
Cl organism = BCFI x C1 water
TCI mg/kg Ip = BCFI L/kg Ip x benchmark value mg/L
1 ' - •
where ,
C1 water = benchmark value in mg/L
Cl = TCI in mg/kg Ip.
with the BCFI for constituents log K 4 to 6 generally estimated using Equation 5-102 and,
for inorganic, metabolizable, and high% lipophilic (i.e., log Kow > ~6) constituents, as
described in Section 5.3.2.
The lexicological benchmark for mammals or birds was also converted to the lipid-
based tissue concentration (TCI in mg/kg Ip) assuming steady-state equilibrium. The TCI
for mammals and birds refers to an acceptable concentration in fish lipids that will not result
in a dose that exceeds the lexicological benchmark, given the exposure assumptions for the
representative species (e.g., body weight, intake). In other words, the tissue concentration
(TCI) in fish for a given receptor is backcalculated from the lexicological benchmark. To
! ' • .
August 1995 5-161
-------
Table
Parameter
togK^
6
P
*
Y
a
P
P
P
a
a
a
to2
<03
a*
5-17. Input Parameters Used to Calculate
Parameter description
Log oclanol/water partition coefficient
In Equation 5-98 defining growth (G)
Allometric growth coefficient (general)
In Equation 5-100 defining respiration rate (r)
Allometric respiration coefficient (general)
Food assimilation efficiency *
Fraction lipid (zooplankton)
> » -
Fraction lipid (small fish)
Fraction lipid (large fish) .*,
Chemical assimilation efficiency (zooplankton)
Chemical assimilation efficiency (small fish)
Chemical assimilation efficiency (large fish)
Weight of zooplankton (trophic level 2)
Weight of small fish (trophic level 3)
Weight of large fish (trophic level 4)
Bioaccumulation
Value
—
0.01
0.2
0.036
0.2
0.8
0.1
0.1
10.1
—
'—
0.1
10
1,000
•
Factors for Limnetic
Units
Unitless
Unitless
Unitless
Unilless
Unitless
Unitless
kg (lp)/kg (w)
kg (lp)/kg (w)
kg (lp)/kg (w)
g assmilld/g ing
g assmilld/g ing
g assmiltd/g ing
g
g
8
Food Chain
Reference
Chemical-specific
Thomann, 1989 1 ,
Thomann, 1989.
Thomann, 1989
Thomann, 1989
Thomann, 1989
Thomann, 1989
Thomann. 1989
Thomann, 1989
Chemical-specific
Chemical-specific
Chemical-specific
Thomann, 1989
Thomann, 1989
Thomann, 1989
.- • •
Ut
D EXPOSURI
VJ
£
o
0
Jf
5-
en
I
1
90
eceptors
-------
Table 5-18.
Name
Acenaphthene
AMrin .
Antimony
Arsenic
Barium
Benz(a)anthracene (1,2-)
Benzo(a)pyrene
Beryllium
Bis(2-ethylhexyl)phthalate
DEHP)
Butylbenzylph thai ale
Cadmium
Chlordane
Chromium VI
Chrysene
Copper
DDT
Di-n-octyl phthalate
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Endosulfan
Endrin
Fluoranthene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Bioaccumulation Factors and
BCF, BAF,
orBSAF
BCF
BAF
BCF
BCF
BCF
BAF
BAF
BCF" "
(also a BAF
, f
BAF
BCF
BAF
BCF
BAF
BCF
BAF
BAF
' BAF
BCF
BCF
BCF
BAF
BAF
BAF
BAF
BAF :
Dissolved
total
d
d
t
t
• t
t
t
t
t
d
t
d
t
t
t
d
t
d
d
d
d
d
t
d
d
d
Bioconcentration Factors
Lipid-bttsed
or or whole-
body
lipid
lipid
whole
whole
whole
lipid
lipid
whole
lipid
lipid
wh^le
lip*d
whole
lipid
whole
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
Trophic
Ievd3
fish
5.700
14.769.414
0
4
ID
800
1.000
19
2.400
28.346
187
4,893,454
1
800
0
53.700.000
2,400
618.762
500
100
3.000
290.126
5,100
155331
"70.313
905.176
for the Limnetic Ecosystem by Trophic Level
Trophic
level 4
fish
5,700
28,894,111
0
4
ID
800
1.000
19
Z400
28,432 .
187
9,255382
1
800
0
100.000,000
2.400
763.219
500
100 .
3.000
318304
5.100
160.813
70.986 "
1,201.943
RBAF
(4/3)
1.00
1.96
1.00
1.00
NA
i.6o
1.00
1.00
•1.00 .
1.00
1.00
1.89
1.00
1.00
1.00
1.86
1.00
•-••"133""
1.00
1.00
1.00
1.10
1.00
1.04
1.01
1.33
tog
4.0
6.5
NA
NA
NA
5.7
6.1
~ NA
7.5
4.4
NA
5.9
NA
5.7
NA
6.9
7.5
5.4
13
1.6
•3.5
5.2
5.2
5.0
4.8
5.5
Source
Predicted; Mackay, 1982
Predicted; Thomann, 1089
Measured; Stephan, 1993
Geomean of measured values
Measured; Stephan, 1993
Default for PAHs; Stephan, 1993
Geomean of measured values
No measured BAF; based on
measured BCF
Predicted; Thomann, 1989
Geomean measured values
Predicted; Thomann, 1989
Geomean of measured values
Measured; Stephan, 1993
Measured; Stephan, 1993
U.S. EPA, 1994b (GLWQI)
No measured BAF; based on
measured BCF
Predicted; Thomann, 1989
Predicted; Veith et ah, 1980
Predicted; Veith et id., 1980
Predicted; Thomann, 1989
Predicted; thomaim, 1989
Measured; based on BCF
Predicted; Thomann, 1989
Predicted; Thomann, 1989
Predicted; Thomann, 1989
(continued) .
tst
o
M
on
g
W
en
a
1
to
1'
s
I
i.
90
•8
o
-------
.Name
Hexachlorbcyclohexane, gamma-
(Lindane)
Hexachlorocyclopentadiene
Hexachlorophene
Kepone
Lead
Mercury
Meihoxychlor
Methyl parathion i
Molybdenum
Nickel
Parathion
Pentachlorobenzene
Pentachlorophenol
Polychlorinated biphenyls (Aroclor
1254)
Selenium
Silver
TCPD. 2,3.7.8-
Toxaphene
Trichlorophenoxyacetic acid, 2.44-
(245-T)
Vanadium
Zinc
ID = insufficient data.
NA = Not applicable.
BCF, BAF,
or BSAF
BAF
BCF
BAF
BAF
BCF
BAF
BAF
"BCF :"
BCF
BCF
BCF
BAF '
BAF
BAF
BCF
BCP
BAF
BAF
BCF
BCF
BAF
Dissolved
total
d
t
t
d
t
t
d
d
t
t
d
d
t
d
t
t
d
t
d
t
t
Table 5-18
Llpkl-based
or or whole-.
body
lipid
lipid
lipid
lipid
whole
whole
lipid
lipid
whole
whole.
fipi!'
lipid
lipid
lipid
whole
whole
lipid
lipid
lipid
whole
whole
(continued)
Trophk
Ievd3
fish
32.600
400
N ND
35,710
44
27,900
38,617
720
ID
1
6.460
202.091
11.990
11.210494
88
ID
15.700.000
40.988.942
1.350
ID
154
Trophk
level 4
fish
32.600
400
ND
35,849
44
140,000
38,780
720
B5
1
6.460
213.463
12.589
22,81 1,266
88
ID
7.850.000
42.349.000
1.350
ID
154
RBAF
(4/3)
1.00
1.00
NA
1.00
1.00
5.02
1.00
1.00
NA -
1.00
1.00
1.06
1.05
2.03
1.00
NA
0.50
1.03
1.00
NA
1.00
tog
K~
3.7
4.9
7.4
4.5
3.8
NA
4.5
2.9
NA
NA
3.8
5.1
5.1
6.3
NA
NA
7.0
5.0
3.1
NA
NA
!
Source
Measured for TL4 fish; Stephan,
1993 .(
Measured; metabolized by fish
(Stephan, 1993) '
Predicted; Thomann, 1989
Stephan. 1993; US FWS. 1988
U.S. EPA. 1994b (GLWQI)
Predicted; Thomann, 1989
Predicted; Thomann, 1989
Measured; Stephan, 1993
Predicted; Thomann, 1989
Predicted; Thomann, 1989
See Toxicotogical Profile
(Appendix B) '
Predicted; Thomann, 1989
Geomean of measured values
•
U.S. EPA, 1994b (GLWQI)
Measured; Stephan. 1993 (level 3
extrapolated from predicted
RBAF)
Predicted; Thomann, 1989
-
Measured; Stephan, 1993
§
5
f
3
»
m
s
0
SO
t
EL
99
8
•8
0
3
-------
5.0 EXPOSURE SJ Concentrations for Ecological Receptors
backcalculate the TCI for mammals, it was conservatively assumed that the diet of the mink
and river otters.in freshwater ecosystems consists of 100 percent fish, with the mink
consuming 100 percent of the fish from the "large fish" trophic level (4) and the otter
consuming 50 percent of the fish from the "small fish" trophic level (3) and SO percent from
the trophic level 4. For birds, it was conservatively assumed that the diet of the
representative species consisted of 100 percent fish, with the eagle and great blue heron
consuming 100 percent of the fish from trophic level 4 and the bsprey, herring gull, and
belted kingfisher consuming 100 percent of the fish from trophic level 3. These assumptions,
while admittedly conservative, were considered appropriate to establish ecological exit criteria
and are consistent with the assumptions contained in the Wildlife Criteria Portions of the
Proposed Water Quality Guidance for the Great Lakes System (U.S. EPA, 1993h).
To calculate the TCI for mammals and birds, it was necessary to first calculate a
whole-body TC by rewriting the equation for dose and solving for the concentration in fish
that will not result in the exceedance of the lexicological benchmark for the representative
species. Dose from contaminated media and food may be represented as:
- «r *C organism) + (7w x C 'water) (5.104)
bw
where
If = intake of contaminated fisb (kg/d)
Iw = intake of contaminated water (L/d)
C organism = whole-body concentration in the fish (mg/kg)
C' water = total concentration in the water (mg/L)
bw = weight of the representative species (kg)
with body weights (bw), food ingestion rates (If and Iw), and drinking rates (Dw) as
summarized in Table 5-19. It should be noted that the exposure inputs were matched to the
lexicological study wherever possible. For example, if a reproductive study on female rats
was used to develop the lexicological benchmark for mink, then the body weight and intake
for female mink were used to backcalculate the dose. Although this consistency is not
believed to result in significant quantitative differences in the dose (because intake is a
function of body weight), in some cases, disproportionate increases in food intake have been
observed in females in certain life stages (e.g., post whelping).
*
Foi chemicals that bioaccumulate significantly in fish tissue, the ingestion of
contaminated food will tend to dominate the exposure (i.e., {If x C organism) » {Iw x C
water}) and, assuming that ingestion is limited to one trophic level, the dose may be rewritten
as:
August 1995 5-165
-------
5.0 EXPOSURE
5.2 Concentrations for Human Receptors
Table 5-19. Exposure Inputs for Representative Species in the
_ - Generic Freshwater Ecosystem
Representative
species
Mink
River otter
Bald eagle
Osprey
female
male
both .
female
male
both
female
male
both
female
male
both
Great blue heron
female
male
both
Mallard
Lesser scaup
Kingfisher
female
male
both
female
male
both
female
male
both
Spotted sandpiper
female
male
both
Herring Gull
female
male
both
Body
weight
(kg)
0.70
1.34
1.02
7,32
8.67
7,99
4.50
3.00
3.75
1.77
1.43
1.63
2.20
2.58
2.34
1.11
1.24
1.16
0.73
0.86
0.75
0.15 f
0.15 f
0.15
0.05 '
0.04
0.04
0.98
1.21
1:09
Water
intake
(L/d)
0.05
0.13
0.081
0.60
0.69
0.65
0.16
0.11
0.14
0.09
0.08
0.08
0.10 e
0.12 e
0.11
0.06
0.07
0.07
0.05
0.05
0.05
0.02 e
0.02 e
0.02
0.01
0.01
0.01
•&06
0.07
0.06
Food
intake
(kg/d)
0.11
021
0.16
1.18
1.35
1.26
0.54
0.36
0.45
0.37
0.30
0.34
0.40
0.46
0.42
031
0.33
0.32-
0.24
026
024
0.07
0.07
0.07
0.03
0.03
0.03
0.19
024
0.21
a
a
a
c
c
d
d
c
c
b
b
b
b
b
b
c
c
b
b
b
Spring/Summer diet
consumption (% voL)
100% fish
(trophic level 3)
100% fish
(0.5 trophic level 3)
(0.5 trophic level 4)
100% fish
(trophic level 4)
100% fish
(trophic level 3)
100% fish
(trophic level 4)
100% aquatic invertebrates
(trophic level 2)
100% aquatic invertebrates
(trophic level 2)
100% fish
(trophic level 3)
100% aquatic invertebrates
(trophic level 2)
100% fish
(trophic level 3)
Wet weight based on allometric equation for diy matter ingestion for dry matter ingestion for eutherian mammals (Nagy,
1987): 0.235 (bw in gins)0-122.
b = Wet weight based on allometric equation for dry matter ingestion for all birds (Nagy. 1987): 0.648 (bw in gins)0-651.
c = Reported foor intake rate was not gender specific.
d = Female osprey food intake rate' was used to estimate food intake rate.
e = Reported water intake rate was not gender.specific.
f = Reported body weight was not gender specific.
August 1995
5-166
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
,o, i» -^ =
-------
5.0 EXPOSURE 5 J Concentrations for Ecological Receptors
As the BAFI equations suggest, using the ratio of the BAF for the trophic level 4 fish
to the BAF for theJrophic level 3 fish (RBAF), the Cl fish (and C fish) for trophic level 4
fish may be written as RBAF4/3 x C fish 3 (i.e., C fish 4 = (RBAF4/3)C fish 3). Rewriting
the equation for the maximum acceptable whole-body fish concentration for ingestion by
mammals:
Q.5Cfish4+0.5Cfish3 * Dosexbw (5-110)
rearranging to solve for C fish in the "small fish" in trophic level 3,
0.5 (RBAF4/3) Cfish 3 + O.SCfish 3 « Dosexbw (5-111)
and revising the equation to solve for the whole-body TC as a function of the benchmark
gives:
Cftsh- D°Sexbw (5-H2)
- (Q.5RBAF4/3+0.5)xIf
Tr _ toxicoiogical benchmark xbw (5-113)
(Q.5RBAF4/3+0.5)xIf
For nonbioaccumulative constituents, TC can be calculated as though all fish are taken from
the same trophic level since bioconcentration does not increase with fish size.* Having
derived the whole-body TC, the TCI was estimated by dividing by the whole-body TC by the
lipid fraction of 0.1 (for all trophic levels) used in the Thomann model. Table 5-20 contains
the values for TC and TCf used to calculate the protective exposure concentrations.
5.3.2.1.3 Estimate Protective Exposure Concentrations
Fish/Aquatic Invertebrates. For bioaccumulative constituents, the dissolved surface
water concentrations that are protective of fish via food chain exposure were calculated by
dividing the TCI (mg/kg) by the BAFI (in L/kg) as follows;
*In practice, whole-body bioconcentration does tend to increase with size since, on average, larger fish have
a higher lipid concentration. However, bioconcentration should not increase with the trophic level of the fish if
normalized on a lipid basis.
August 1995 5-168
-------
Table 5-20.
Constituent name
Acenaphlhene
AJdrin
Antimony
Arsenic
lBenz(»)jinthr»cenc (1.2-j
Benzo(a)pyTene
Whole-Body and
RBAF4/3
1.00
0.87
1.00
1.00
1.00
. 1.68
Bis(2-€thyihexyi)phihaiate (also DEHP) 1.00
Butylbenzyl phthalale
Cadmium i
Chlordane
Chromium VI
Chrysene
Copper
"DDT' ' "'" ' "
Di-n-octyl phthalate
DieMrin
Diethyl phthalate
Dimethyl phthalate
Endosulfan
Endrin
Fluoranthene
Hexachlorobenzene
Hexachlorocyclohexane, gamma-
Hexachiorocyclopentadiene
Hexachlorophene
Kepone
Lead
0.95
1.00
1.08
1.00
1.00
1.00
i.86
1.00
0.96
1.00
1.00
1.00
0.92
_ -1SSI~
~~ "1.33
(Lindane) ID
0.91
ID •
0.94
1.00
Lipid-Based Tissue
(all units
Mink
TC
ID
3.3e-01
S.le-01
2.5e+01
ID
2.8e+OO
2.0e+02
l.Oe+03
S.le+00
1.2e401
7.4e+00
ID
l.le+OI
, 2.3e4OO
ID
6.3e-02
ID
ID
l.Se+01
i.4e+66
ID
7.5e*00
ID
2.4e+02
8.4e+00
3.8e+00
10e-02
Mink
ra
a>
3.3e+OO
• NA
NA
ID
2.8e+Ol
XOe+O3
1.0e-fO4
NA
1.2e+02
NA
":• ID
NA '
2.3e+Ol
ID
' 6.3e-01
ID
ID
1.8e+O2
1.4e+01
ID
7.5e+01
ID
2.4e»03
8.4C401
3.8e+01
NA
Concentration
in mg/kg)
for Prey
River otter River otter
TC TCI
ID
1.2e-01
2.5e-01
8.5e+00
ID
9.4e-01
6.5e+01
3.5e+02
1.8e+00
3.8e+0b
2.7e+00
ID
3.6e+00
4.5e-01
ID
2.2e-02
ID.
ID
6.6e-»00
S.Oe-01 :
ID
2.1efOO
ID
8.5e+01
ID
1.4e400
3.3e-02
ID
1.2e+00
NA
NA
"ID " "
9.4etOO
6.5e+02
3.5e+O3
NA
3.8e+Ol
NA
ID
NA
4.5e+00
ID
Z2e-01
ID
ID
6.6e+01
5.0e+00
ID
Xle+01
n>
8.5e402
ID
1.4e4Ol
NA
in the
Limnetic Ecosystem
Bald eagle Bald eagle
TC TCI
ID
3.3e-02
ID
3.3e*01
~~TD "
ID
ID
ID
1.2e401
ID
ID
ID
ID
1.7e4)l
ID
3.3e-01
ID
ID
ID
1.7e-01
ID
12e*00
4.5e+00
ID
ID
3.4e+01
4.9e-02
ID
3.3e^bi
NA
NA
ID
ID
ID
ID
NA
ID
NA
ID
NA
1.7e+00
ID
3.3e-K»
ID
ID
ID
1.7e+00
ID
2.2e+01
4.5e+01
ID
ID
3.4e+02
NA
Osprey
TC
ID
2.4e-02
ID i
2.4e+di
ID
ID
ID
no.
S.le+OO
ID
ID
ID
ID
9.5e-02
ID
1.9e-OI
ID
ID
ID
1.4e-01
ID
1.5e+00
3.2e+00
ID
ID
X4e+01
3.6e-02
Osprey -
TCI
ID
...t.
1 14e-0l
NA
NA
ID
ID
ID
ID
NA
ID
NA
ID
NA
9.5e-01
ID
1.9e+00
ID
ID
ID
1.4e-K»
ID
LSe^Ol
3.2e+01
ID
ID
14e-f02
NA
(continued)
5.0 EXPOSURE 5 J Concentrations for Ecological Receptors
-------
Table 5-20
Constituent name
Mercury
Methyl pumthion
Molybdenum
Nickel
Parathion
Pentachiorobenzene
PenUchlorophenol
Poiychiorinaied biphenyb (Arock>r-1254)
Selenium
TCDD. 2.3.7,8- J&
foxaphene ' *
f richiorophenoxyacetic acid, 2.4J5-
Zinc
RBAF4/3
5.02
1.00
NA
. 1.00
1.00
6.91
1.05
103
1.00
0.99
1.03
1.00
1.00
Mink
TC
1.9e+00
l.le+01
4.7e-01
lle+02
2.8e+00
13e401
1.9e+01
l.Oe+00
1.9e-01
2.7e-06
7.5e+00
1.7e402
8.9e*02
Mink
TCI
NA
l.le*02
NA
NA
18e*01
13e+02
1.9e+02
l.Oe+01
NA
17e-05
7.5e+OI
1.7e+03
•J NA
V . '
(continued)
River otter
TC
l.oc-01
3.8e+00
ID
7.2e401
9.8e-01
rfctib
6.3c400
l.9e-01
7.2e-02
9.5e-07
14e+OO
5.6e+OI
19e+O2
River otter
TCI
NA
3.8e+01
NA
NA
9.8e+00
7.7e*01
6.3e+01
1.9e+00
NA
9.5e-06
2.4e+Ol
5.6e^O2
NA
Bald eagle
TC
4.2e-02
19e+OO
ID
ID
l.Oe+01
m
1.3e»01
l.Oe+00
6.1e+OO
1.2e-04
15e4)l
ID
ID
BaU eagle
TCI
NA
2.9e+01
NA
NA
l.Oe+02
"iD""";
1.3e+O2
l.Oe+01
NA
1.2e-03
15e^00
ID
NA
Osprey
TC
19e-02
lOe+00
DO
IDs
7.3e+00
ID
2.0e402
7.6e-01
4.3e4OO
5.7e-05
1.4e-01
- ID
ID
Osprey
TCI
NA
lOe+01 .
NA
NA
7.3e+01
ID
10e403
7.6e+00
NA
5.7e-04
1.4e+00
ID
NA "
-
Constituent name
Acenaphthene
AHrm "'"
Antimony
Arsenic
Benz(a)inmracene 02-)
fienzoliOpyicne *
Bis(2-ethylhexyl)phthalate (also DEHP)
Butylbenzyl phtnalate
Cadmium
Chlordane
Chromium VI
Chrytene
Copper
Great blue
heron TC
ID
:"" ID"
a" .'""
16e*01
ID
V ID'
ID
ID
8.9e+<
ID
.ID
ID
ID
Great blue
heron TCI
ID
18*01
NA
NA~~
ID"
ID""
ID
ID
NA
ID
NA
ID
NA
Kingfisher
TC
ID
IJfc-02
ID
1.8e+01
"ID' •
ID ;'"
ID
ID
-------
J.
Constituent name
DDT
Di-n-octyi phthalate
DieUrin
Diethyl phthalate
Dimethyl phthalate
Endotulfan
Endrin
Flubrmnthene
Hexachlorobenzene
Hexachlorocyclohexane, gamma- (Lindane)
HexachlorocyclopenUdiene , f
Hexachlofophene
Kepone
Lead
Mercury
Methyl parathion
Molybdenum
"Nickel ;
Pwathion
Pentachlorobenzene
Pentachloibphenot
Polydilorinated biphenyb (Aroclor-1254)
Selenium
TCDD; 2,3,7,8-
Toxaphene
Trichlorophenoxyacetic acid, 2,4,5-
Zuic
ID « Insufficient data.
NA = Not applicable.
Great blue
heron TC
l.le-01
-'ID
2.2e-01
ID
" ID
ID
l.le-61
ID
1.6e+00
3.6e+00
ID " "
m
15*401
19e4i
18*32
23*400
ID
ID
8.1e+00
ID
£1*402
8.3*-01
4.6*400
o.leJ05
1.7e-01
ID
ID
Table 5-20
Great blue
heron TO
1,1*400
ID
12*400
ID
ID
1.5*-01
ID
1.8*400
4.1*400
ID
ff)
19*401
4.5*^02
3.6*^61
2.4*400
ID
ID
9.1*400
ID
13*402
9i*-01
5.i*400
"" T3&X
lle-01
ID
ID
Herring
gull TCI
1.0*400
ID
16*400
ID
ID
ID
1.5*400
ID
1.8*401
4.1*401
ID
ID
19*402
NA
NA
14*401
NA
NA
9.1*401
ID
13*403
9.2*400
NA
7.2*^64
11*400
ID
'NA
Fish
TC
5.6e-01
ID
8.9e-01 f
S.4«401 .
1,7*^04
1.7*-O2
6.5e-01
3.1*400
10*402
3.8e-02
3.0*^)2
m
1.0*400
1.4*^01
1.5*401
13e-03
"; ro
1.3*31
8.2e-03
18*404
5.1*400
8.2e-01
4.4e-01
ID
5.3e-01
1.4*400
1.8*401
Fish
Ta
5.6*400
ID
8.9*400
h T.4*402
1.7*405
1.7*-OI
6.5*400
3.1*401
2.0*403
3Hf*-Or~
3.0c-0i
'" ID
1.0*401
NA
NA
13e-02
NA
'""NA
8.2e-02
"2S*40T~
5:i*4
-------
5.0 EXPOSURE
5.3 Concentrations for Ecological Receptors
If the BAFf was a measured value, the protective concentration was considered to be total
and was represented by
For nonbioaccumulative constituents, the benchmarks for fish/aquatic invertebrates were
adopted as the protective exposure concentration for these ecological receptors, respectively
(see Table 4-4).
Mammals and Birds. For bioaccumulative contaminants, protective exposure
concentrations for mammals and birds were backcalculated by dividing the TCI by the BAFI
or the BCFf, respectively, as shown in Equation 5-114. As stated above, measured BAFfs
were assumed to be based on the total water column concentration and, therefore, C^ was a
total water concentration. Similarly, measured bioconcentratibn factors for metals were
assumed to be based on total water concentrations unless specified otherwise in the study or
studies. For organic chemicals with log Kow values below 4.0, measured and predicted BCFs
were assumed to reflect dissolved water concentrations.
It is important to emphasize that because a chemical is not considered bioaccumulative
does not indicate that the dietary exposure is insignificant Consequently, both surface water
ingestion and dietary intake were considered in calculating protective exposure concentrations
for nonbioaccumulative constituents. Because bioconcentration is a function of lipid content
and dissolved water concentration, the trophic level preferences of piscivorous animals is not
needed. The Cdpro for nonbioaccumulative organic constituents was calculated by rewriting
Equation 5-114 as shown below41:
where
Iff
Iw
benchmark
bw
BCF«
pr.o
benchmark x bw
Iw + (7/J x BCFV)
(5-115)
intake of contaminated fish lipids (kg Ip/d)
intake of contaminated water (L/d)
lexicological benchmark (mg/kg-d)
weight of the representative species (kg)
lipid-based bioconcentration factor (L/kg lp).
*For nonbioaccumulative metals, intake of whole contaminated fish and a whole-body BCF were used to
calculate C_ for the total water concentration.
August 1995
5-172
-------
5.0 EXPOSURE 5 J Concentrations for Ecological Receptors
Aquatic Plants, The protective exposure concentrations for aquatic plants (algae or
vascular plants) arerdescribed in Section 4 on ecotoxicological benchmarks. Briefly, the
lowest effects concentration was selected to represent the aquatic plant community for effects
levels corresponding to roughly 20 percent (e.g., 20 percent decrease in cell number).
5.32.2 Littoral Ecosystem , - .
The methods used to backcalculate protective exposure concentrations for wildlife
associated with the littoral ecosystem were similar to those used for the limnetic ecosystem
except for: (1) the model used to estimate bioaccumulation factors, (2) three additional avian
receptors were added to represent species that feed largely on aquatic invertebrates and
sediment dwellers, and (3) sediment organisms (i.e., benthos) were evaluated for direct
contact
Since the littoral food web was developed from the limnetic food chain, the three basic
steps (i.e., calculation of BAFfe, determinatio-. of TCfe, and calculation of protective
exposure concentrations) described above for the limnetic food chain are also applicable. For
completeness, the calculation of bioaccumulation factors includes relevant equations on the
derivation of lipid-based biota sediment accumulation factors (BSAFIs) and equations that
bear directly on these algorithms. However, equations have not been included on those
parameters (e.g., Ki, Gi, a BCFf) that were calculated using the methodology presented for
the limnetic food chain. The section on TCI derivation was limited to a discussion of the
TCI for the river otter since the assumptions on lipid fraction in fish in the upper trophic
levels are different than those in the limnetic food chain.
5.32.2.1 Estimation of Bioaccumulation Factors
The littoral zone was represented by the freshwater food web and includes
phytoplankton, zooplankton, benthic Vertebrates, forage fish, and large fish.* As with the
limnetic food chain, the highest level fish are assumed to be piscivores and feed primarily .on
smaller forage fish in the lower trophic levels. The lower trophic level fish (forage fish) are
assumed to consume both plankton and benthic invertebrates. Thomann et al. (1992)
developed a model for estimating BAFs for hydrophobia organic chemicals in the simple
*As defined by Thomann et al. (1992), the term "benthic invertebrates" refers primarily to amphipods and
does not include other species in the benthic community such as molluscs.
August 1995 5-173
-------
5.0 EXPOSURE
5J Concentrations for Ecological Receptors
littoral food web depicted in Figure 5-4. The littoral food web is, in essence, a freshwater
food web with sediment interaction that accounts for sediment bioaccumulation as well as
transfer through the food chain. This model was used as the basis for calculating exposures
to mammals, birds, and fish that rely on prey from the littoral food web.
In order to provide a generic modeling framework that accounts for exposures via
water, food chain, and sediments, a normalization must be carried out either to the sediment
concentration or to the overlying water concentration. Under certain assumptions described
below, this normalization will eliminate site specificity.
As stated above, Thomann (1989) defined the the lipid-based bioaccumulation factor
(BAR) as:
(5-116)
C water
where Cl organism is the lipid-based concentration resulting from the water and food routes
of exposure. Similarly, the lipid-based biota sediment accumulation factor (BSAFI) or
accumulation due to sediment uptake is defined as:
Source: Thornann et al. (1992).
Figure 5-4. Schematic of a five-compartment littoral food-web model.
August 1995 - 5-174
-------
5.0 EXPOSURE 5 J Concentrations for Ecological Receptors
BSAFiL/kg Ip-""***** (5-117)
sediment
oc
where Cx sediment is the sediment concentration normalized to the organic carbon content
In addition to these assumptions, let BCFf be defined as in Thomann (1989):
BCFt= (5-118)
Ki+Gi
with the sediment/interstitial water partition coefficient (ics) equal to the ratio between the
sediment chemical concentration (on an organic carbon basis) to the interstitial freely
dissolved chemical concentration as given by
C^sediment ' _
C interstitial water (i\v)
and the sediment/overlying water partition coefficient (nws) equal to the ratio between the
sediment chemical concentration (organic carbon basis) to the overlying-water freely dissolved
concentration defined as
^sediment
nws ^ A - ^ '
C 'water
Note also that the relationship between the lipid-based bioaccumulation factor and lipid-based
biota sediment accumulation factor is a function of the sediment/overlying water partition
coefficient:
BAFl*KwsBSAFt . (5-121)
Solving the differential equations (see Thomann et aL, 1992) for each compartment
(i.e., dCI organism/df) and rearranging to solve for the BSAFI:
August 1995 5-175
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
Trophic Level 1: phytoplankton
BASFll = BCni (5-122)
Trophic Level 2: zooplankton
BCF,
(5-123)
Trophic Level. 2: benthic invertebrates
V ws
where K' is the relative water exposure route partition coefficient and is given by
and .
b2te = fraction of uptake from 'sediment
b2bw = fraction of uptake from overlying water (b2bs :+ b2bw = 1)
Trophic Level 3: forage fish
BSAFG*2£!l!L + g3iBSAFa+g3ilpSAFt2b (5-125)
*"*..'
Trophic Level 4: large fish
<*te
(5-126)
For each compartment, the transfer of chemical from one trophic level to the next is
represented by g and given by the following equation:
August 1995 5-176
-------
5.0 EXPOSURE
5J Concentrations for Ecological Receptors
where
ai, Ki and Gi
II
Ki+Gi
(5-127)
= food preference for a compartment in the previous trophic level (or for
sediment vs. plankton for the benthic invertebrates)
= defined as in the limnetic food chain
= lipid-specific consumption rate.
The lipid-specific consumption rate (II in gm {wet}i/gm (wet)i-l per day) for nonbenthic
organisms is defined as
a
(5-128)
and the organic-carbon-specific consumption rate (1^. in gm organic C ingested/gm lipid i per
day) for benthos feeding on sediment is defined as .
(C+r)
P'
(5-129)
where
G, r, a, and p
Poc
defined as in Thomann (1989)
fraction organic carbon of the predator
wet- to dry-weight ratios for predator and prey, respectively.
Recalling that the BAFI may be estimated by multiplying the BSAFI by the
sediment/overlying water partition coefficient (rcwg), the BAFI equation for each compartment
was derived (Equations 5-130 to 5-133). Notice that, for the BAFI equations for trophic
levels 3 and 4, the first term in brackets is identical to the equation for the limnetic food
chain. Thus, the second term in braces represents the contribution due to sediment interaction
with, and consumption of, benthic organisms.
August 1995
5-177
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
g2lBCFll (5-130)
BAFl2b -BCFl2b[*Jif] +£2**™ ^g^jBCFtl * (5-131)
BAFU = [BCF13 + g37BCFt2 + g32g2lBCFll} «• '
BAFl4~(BCFl4+g43BCFU+g43g32BCFl2+g43g32g2lBCFll} +
Since the accumulation due to the consumption of benthic organisms (i.e., second term in
braces) in the BAFI equations depends upon the sediment partition coefficients Jts, jcw, and
jcws, the derivation of each coefficient is discussed below.
Sediment/Interstitial Water Partition Coefficient (nj; Assuming that 1 L organic
carbon is approximately equivalent to 1 L octanol, ns (on an organic carbon basis) is
equivalent to the octanol/water partition coefficient, KDW, or
V ~
SedimentAVater Column Partition Coefficient (TCW). As Thomann et al. (1992) point
out, the water column partition coefficient can be calculated by using the formulation of Di
Toro (1985), which incorporates a "solids concentration effect" and is given by
c = _ ^T __ (5-134)
w
where
f^w = fraction organic carbon of the water column participates
. mw = water column suspended solids
1.4 = an empirical constant (Di Toro et al., 1991).
Sediment/Overlying Water Partition Coefficient OO- Having defined Jtw, KWS may
be defined by converting to the organic carbon basis for the sediment
August 1995 5-178
-------
5.0 EXPOSURE
5J Concentrations for Ecological Receptors
where
= as defined above
= fraction organic carbon in the sediment
and
(5-136)
where
vu
vd
ds
H. =
= resuspension velocity of particulatcs
= deposition velocity of participates
= fraction of chemical in paniculate form
= fraction of chemical in dissolved form
= interstitial diffusion rate
= sediment decay rate
sediment porosity
sediment depth.
Note that if the sediment decay rate is equal to zero, as it is for metals and many
persistent, hydrophobic organics, the sediment depth does not influence the calculation of 5
and the calculation is greatly simplified. The relationships described above were used to
calculate the ratio of sediment particulate concentration to overlying-water dissolved
concentration (i.e., partition coefficients) for the BAFI algorithms presented above.
Table 5-21 presents the input parameters used to calculate the lipid-based
bioaccumulation factors for the littoral food web consisting of phytoplankton—zooplankton,
benthic invertebrates—forage fish—piscivorous fish. These input parameters are identical to
the parameters used to calibrate the aquatic food web model developed by Thomann et al.
(1992).
It is important to note that several parameters, specifically lipid fraction, weight, and
food assimilation efficiency have been changed from the values used for the limnetic food
chain. For example, the limnetic foo4«phain set the lipid fraction at 0.1 across all trophic
levels and the littoral food web used species-specific lipid fractions varying from 0.05 to 0.2.
In addition, the weight of the forage fish (small fish) was increased from 10 grams to 100
grains for the littoral food web. Values for input parameters were selected to be consistent
within the models rather than across the generic ecosystems so that: (1) the model results
could be reproduced and validated-with actual study data and (2) the methodologies could be
validated even if species-specific input assumptions were changed. The BCFIs, BAFIs, and
BSAFs for trophic levels 2 through 4 (including benthos) are presented in Table 5-22. The
August 1995
5-179
-------
5.0 EXPOSURE
5J Concentrations for Ecological Receptors
Table 5-2 L. Input Parameters Used to Calculate Lipid-Based Bioaccumulation
Factors for the Littoral Food Web
Parameter
(ogK,,
5
P
*
a
a
a
a
P
P
P
P
P
a
a
a
a
w2
wOh
W£0
w3
w4
focw
foes
mw
m»
(
Kf
vd
vu
kds
fps
fds
Ht
b2bs
b2bw
p2b*
p2b1
p32b
p32
Parameter description
Log octanol/Watar partition coeff.
In eq. defining growth rate (Q)
Growth coefficient (garwral)
In eq. defining respiration rat* (r)
RoAfiafAiiMi coofReiBWit /{uuimft
MfJVeMlvVI tAMHIIWIIl lUVINHVlJ
Food assimilation affioiancy (zooplankbn)
Food assimilation aHciancy (benMc invert)
Food assimilation efficiency (foraga fish)
Food assimilation affioiancy (largo fish)
Fraction lipkl (phytoplankton)
Fraction lipid (zooplankton)
Fraction lipid (banttuc invertebrates)
Fraction lipid (foraga fish)
Fraction lipid (larga fish)
Cham, assim. aff. (zooplankton)
Cham, assim. aff. (benthic invertebrates)
Cham, assim. aff. (foraga fish)
Cham, assim. aff. (piscivorous fish)
Weight of zooptankton
UUoirrfit af hantfiir invwt^mta*
Waight of foraga fish
Weight of piscivorous fish
Frac organic C in water column particulatas
Frac organic C in ttw sadmant
[Suspended solids] in water column
[Suspended solids] in sad interstitial water
Sedunent porosity
Interstitial diffusion rats
Nat deposition velocity
Resuspension vatooity
Sediment decay rata
Fraction of chemical in-particulala form
Fraction of chemical in dissolved form
Sedunent depth
Fraction uptake from sed interstitial water
Fraction uptake from overlying water
Feeding preference for sedimer*»
Feeding preference for phytoplankton
Feeding preference for benthic invert
Feeding preference for zooplankton
Value
. —
0.01
0.2
0.036
n 9
V.A
0.3
0.2
0.8
0.8
0.01
O.OS
0.03
0.08
0.2
—
—
—
—
0.01
nnr)9
V.MJC
100
1.000
0.2
0.04
1.0E-06
5.2E-01
0.8
1
0.1
0.3
0.0
—
—
1
0.2
0.8
0.2
0.8
0.7
0.3
Untie
Unittess
Untttess
UnrtJess
UnMess
Unilfau
UIIIIMPM
UmtJess
UnMass
Unrttoss
UnMess
UnrtJess
kgOpWg(w)
kg(lpykg(w)
kg (lp)/V
-------
Table 5-22. Biota-
Name
Acenaphthene
AWrin
Antimony
Anenic
Barium
Benz(a)anthracene (1,2-)
Benzo(a)pyrene
Beryllium
Bis(2-ethylhexyl)phihaiate
DEHP)
Butylbenzylphthalate
Cadmium
Chtordane
Chromium VI
Chiyiene
Copper
DOT
Di-n-octyl phthalate
Dkldrin
Dkthyl phthalate
Dimethyl phthalate
Endosulfan
Endrin
Fluoranthene
Heptachtor
Heptachlor epoxide
Hexachlorobenzene
Sediment Accumulation Factors
BCP, BAF,
or BSAF
BCF
BAF
BCF
BCF
BCF
BAF
BAF
BCF
(also BAP
..I
BAF
BCF
BAF
BCF
BAF
BCF
BAF
BAF
HBAF
BCP
BCF
BCF
BAF
BAF
BAF
BAF
BAF
and Bioaccumulation Factors
Llpkl-based
Dissolved or ' or whole-
total body
d
d
t
d
t
d
t
t
I
d
t
d
d
d
d
d
t
d
d
d
lipid
lipid
whole
whole
whole
lipid
lipid
whole
lipid
lipid
whole
iipidi
whole
lipid
whole
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
lipid
Trophic
level 2
Inverts
ID
52,309,538
ID
ID
ID
ID
ID
ID
ID
55,622
ID
10.200.120
ID
ID
ID
n>
ID
1,322,661
ID
ID
ID
643.213
ID
336,430
146.934
1.918.811
Trophk
level 3
fish
5.700
48.382,161
0
4
ID
800
1,000
19
2,400
28.191
187
8,889,256
1
800
0
53.700,000
2,400
742J34
500
100
3,000
322,806
5,100
160408
70,553
1,160307
for the Littoral
Trophk
level 4
fish
5,700
41,938.806
0
4
ID
800
1.000
19
2,400
26.782
187
9,580,446
.1
800
0
100.000.000
2,400
713,460
500
100
3.000
298.094
5.100
145.790
64.836
1,142,641
RBAF
(4/3)
1.00
0.87
1.00
1.00
NA
1.00
1.00
1.00
1.00
0.95
1.00
1.08
1.00
1.00
1.00
1.86
1.00
6.96
1.00
1.00
1.00
0.92
1.00
0.91
0.92
0.98
Ecosystem by Trophic Level
Source
Predicted; Mackay. 1982
Predicted; Thomann et aU 1992
Measured; Stephan, 1993 '
Geomean of measured values
Measured; Stephan, 1993
Default for PAHs; Stephan. 1993
Geomean of measured values
No measured BAF; based on
measured BCF
Predicted; Thomann et al., 1992
Geomean measured values
Predicted; Thomann et al.. 1992
Geomean of measured values
Measured; Stephan, 1993
Measured; Stephan, 1993
VS. EPA. 1994b (GLWQI)
No measured BAF; based on
measured BCF
Predicted; Thomann et al.. 1992
Predicted; Veith et al., 1979
Predicted; Veith et al. 1979
Predicted; Thomann, 1989
Predicted; Thomann et al, 1992
Measured; based on BCP
Predicted; Thomann et al., 1992
Predicted; Thomann et al., 1992
Predicted; Thomann. 1992 -
' - (continued)
5.0 EXPOSURE 5.3 Concentrations for Ecological Receptors
-------
Table 5-22 (continued)
Name
Hexachlorocyclohexane, gamma-
(Lindane)
Hexachlorocyctopentadiene
Hexachlbrophene
Kepone
Lead
Merctny
Melhoxchlor
Methyl parathion •. f
Molybdenum
Nickel
Parathion
Pentachlorobenzene
Pentachlorophenol
Polychlorinated biphenyls (Aroclor
1254)
Selenium
Silver
TCDD. 2,3.7.8-
toxaphene
Trichlorophenoxyacetic acid, 2,4,5-
(245-T)
Vanadium
Zinc
BCF, BAF,
orBSAF
BAF
BCF
BAF
BAF
BCF
BAF
BAF
BCF
BCF
BCF
BCF
BAF
BAF
BAF
BCF
BCF
BSAF
BAF
BCF
BCF
BCF
Dissolved or
total
d
t .
t
d
t
t
d
d
t
t
d
d
t
~d'~
t
t
d
t
d
t
t
Llpid-based
or whole-
body
lipid
lipid
lipid
lipid
whole
whole
lipid
lipid
whole
whole
lipid •'
lipid
lipid
lipid
whole
whole
lipid
lipid
lipid
whole
whole
Trophic
level!
inverts '
ID
ID
ID
71,181
ID
ID
77,398
"ftr
ID
ID
ID
439.866
ID
32.153368
ID
ID
0.48
ID
ID
ID
ID
Trophic
level 3
fish
32,600
400
ID
35,541
44
27,900
38,449
720
ID
1
6,460
212,985
ID
30.680,700
88
ID
0.068
40.988,942
1,350
ID
154
Trophk
level 4
fish
32.600
400
ID
33.475
44
140,000
36,107
720
.ID
1
6,460
194,236
ID
29.494339
88
ID
0.067
42,349.000
1.350
ID
154
RBAF
(4A3)
1.00
1.00
NA
0.94
1.00
5.02
0.94
1.00
NA
1.00
1.00
0.91
NA
0.96.
1.00
NA
0.99
1.03
1.00
NA
1.00
Source
Measured for TL4 fish; Stephan, 1993
r
Measured; metabolized by fish
(Stephan. 1993) j
•
Predicted; Thomann et al.. 1992
Stephan, 1993
U.S. EPA. i994b (GLWQij
Predicted; Thomann et al., 1992
Predicted; Thomann, 1989
Measured; Stephan, 1993
Predicted; Thomann. 1989
Predicted; Thomann et al.. 1992
Predicted; Thomann et al.. 1992
Geomean of measured values
Level 2 ft 3 - measured values (Cook
et al.. 1993); level 4 - USEPA ORD,
1995
Measured; Stephan, 1993 (level 3
extrapolated from predicted RBAF)
Predicted; Thomann. 1989
- . ' •
Measured; Stephan, 1993
ID = Insufficient data. . ' , <
NA = Not applicable.
-
5.0 EXPOSURE . 5J Concentrations for Ecological Receptors
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
table also identifies each value as relevant to dissolved or total water concentrations and
includes the souree~of each value (i.e., calculated or measured). ,
5.3.2.2 J Calculation of Tissue Concentration
Virtually all of the TCs and TCI values shown in Table 5-23 for fish, mammals, and
birds remained the same as those values calculated for the limnetic food chain. The littoral
ecosystem included three additional avian receptors that feed primarily on invertebrate species
found in sediment The TCI values for the mallard and lesser scaup were calculated without
adjusting for trophic level as it was assumed that these species feed exclusively on benthic
invertebrates. However, the TCI for the river otter was adjusted to account for the
differences in lipid fraction assumed for trophic levels in the littoral food web.
In Section 5.3.2.1, it was noted that at a constant C1 water (assuming steady-state), the
ratio of BAFI for trophic levels 4 and 3 is approximately equal to the ratio of Cl organism
for trophic levels 4 and 3. Since the river otter was assumed to take 50 percent of its fish
from trophic level 3 and 50 percent from trophic level 4, this ratio was used to define Cl fish
4 in terms of Cl fish 3 and solve for the whole-body TC. The constant lipid fraction of 0.1
assumed for both trophic levels 3 and 4 obviated the normalization of bioaccumulation factors
to derive whole-body organism concentrations. However, the littoral food web model used a
lipid fraction of 0.08 and 0.2 for trophic levels 3 and 4 fish, respectively. Therefore, the
BAFI values were normalized by multiplying the RBAF4/3 by the ratio.of the lipid fraction
in trophic level 4 (i.e., 0.2) to the lipid fraction in trophic level 3 (i.e., 0.08), or 2.5. In other
words, 2.5RBAF4/3 was substituted for RBAc4/3 used to calculate Cfish in the limnetic food
chain. Multiplying the 0.5 fraction for trophic level 4 fish by the lipid fraction ratio of 2.5
results in the following equation to calculate the TC for the river otter associated with the
littoral food web:
-,£_ benchmarkxbw , (5-137)
" (1.25RBAF4/3 +Q.5)xlf
As in the limnetic food chain, TCb were estimated by dividing the whole-body TC by the
lipid fraction appropriate to the prey item consumed For example, since the eagle is assumed
to eat only trophic level 4 fish, the TCI was calculated by dividing the TC by the 0.2 lipid
fraction. Similarly, the river otter TCI was calculated by dividing the TC by the lipid
fraction of 0.08 since TC is expressed in terms of trophic level 3 fish (i.e., small fish or
forage fish). The TCIs for the mallafg? lesser scaup, and spotted sandpiper were calculated
by dividing the TC by the lipid fraction assumed for invertebrates, 0.03.
5.3.2.2.3 Estimate Protective Exposure Concentrations
Fishy Aquatic Organisms and Benthos. For bioaccumulative constituents, the
dissolved surface water concentrations that are protective of fish were calculated by dividing
the TCI (mg/kg) by the BAFI (in Ukg) as follows:
August 1995 5-183
-------
Table 5-23.
Constituent name
Acenaphlhene
Aldrin
Antimony
Arsenic
Barium
Benz(a)anthracene (14-)
Benzo(a)pvrene
Beryllium
Bis(2-ethylhexyl)phthalale (also |
DEJff) • »
Butylbenzyr phlhalate
Cadmium
Chlordane.
Chromium VI
Chrysene
Copper
DDT
Di-n-octyl phthalate
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Endosulfan
Endrin
Fiuoranthene
Heptachlor
Hepuchlor epoxide
Hexachlorobenzene
Whole-Body and
RBAF
4/3
1.00
0.87
1.00
1.00
ED
1.00
1.00
1.00
1.00
0.95
1.00
To*
1.00
1.00
Mink
TC
ED
3.3e-01
8.1e-01
2.5e+01
ID
ED
18e+00
ID
2.0e*02
l.Oe+03
5.le+00
1.2e*01
7.4e*00
_ jg.
1.00 l.le+01
1.86
1.00
0.96
1.00
1.00
1.00
0.92
1.00
0.91
0.92
0.98
2.3e-»00
ED
6.3e-02
CD
ID
1.8e+OI
1.4fr»00
ID
2.1e+00 .
ED
7.3e*00
Lipid-Based Tissue
(all units
Mink
TCI
ID
4.1e+00
NA
NA
NA
ED
3.4e+01
NA
15e+03
1.3e+04
NA
Ue+02
NA
ED
NA
2.9e+01
ED
7.8e-01
CD
ED
2.3e+02
1.8e+01
ED
16e+01
ED
9.4e+01
Concentration for Prey in
in mg/kg)
River River oner Bald
otter TC TCI eagle TC
CD
1.2e-01
2.5e-01
8Je»00
ED
ED
9.4e-01
ED
6.5e*01
3JfrtO2
1.8e+]0
3.8e+00
2.7e+00
ED
3.6e*00
4.5e-01
ED
12e-02
ED
ED
6.6*K»
......._...
CD
7.7er01
ED
16e+00
ED ED
1.5e400 3.3e-02
NA CD
NA 3.3e^01
NA 3.4e-»02
ED ED
1.2e+01 ED
NA CD
8.1C+O2 ED
43e+03 ID
NA 1.2*f01
4.7e+01 ED
NA ID
ED ED
NA ED
5.6e+00 1.7e-01
ED ED
Z8e-01 3.3e-01
ED CD
ED CD
^.3e+01 ED
6.2e+00 1.7e-01
ID ED.
9.7e+00 ED
ED ED
3.2e+01 2.2e+00
Bald eagle
TCI
ED
1.7e-01
NA
NA
NA
ED
ED
NA
ED
ED
NA
ED
NA
ED
NA
8.3e-01
ED
1.7e+00
CD
CD
ED
8.3e-01
ED
CD
CD
l.le+01
the Littoral Ecosystem
Osprej
TC
ED
14e-02
ED
2.4e+01
2^e-»02
ED
CD
ED
CD
ED
8.1e+00
ED
ED
ED
ED
9.5e-02
ED
1.9e-01
CD
ED
ED
1.4e-01
ED
ED
ED
1.5e+00
Osprey
TQ
ED
3.0e-01
NA
NA
NA
ED
ED
NA
ED
CD
NA
ED
NA
ED
NA
1.2e+00
ED
14e+00
ED
ID
ED
1.8e+00
ED
ID
ED
1.9e+01
Great blue
heron TC
ED
Z8e-02
EDi
Z6e+0l
^ *|»A/^^
4r, /CTV/A>
ED
ED
CD
ED
ED
8.9efOO
ED
ED
ED
ED
l.le-01
ED
2.2e-01
CD
CO
ED .
l.le-01
ED
ED
ED .
1.6V»00
Great blue
heron TCI
ED
'( i.4e^bi
NA
NA
NA
ED
ED
NA
ED
ED
NA
ED
NA
ED
NA
5.6e-01
ID
l.le+00
ED
CD
ED
5.6eX)i
m
CD
ED
8.1e+00
(continued)
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
-------
Constituent name
HexachJorocyclohexane,
gamma- (Lindane)
twxaciuorocyciopeniaaiene
rlexachiorophene
Kepone
Lead
Mercmy
Mcthoxychlor
Methyl panlhion
Molybdenum
Nickel
Parathion
Pentachlorobenzene
Pentachlorophenol
Poiychknuiated biphenyl* •
(Arodor-1254)
Selenium
Silver
TCDD, Z3.7.8-
.Toxaphene
Trichloropbenoxyacetic acid,
2.4,5-
Vanadium
Zinc
RBAF
4/3
1.00
1.00
ID
0.94
1.00
162
0.94
1.00
ID
. 1 I-00
1.00
0.91
ID
096
1.00
ID
0.99
1.03
1.00
ID
1.00
Mink
TC
ID
14*402
8.4e400
3.8*400 '
ZOe-02
1.9*400
4.5*402
1.1C401
4.7e-01
11*402
18*400
23*401 .
1.9*401
i:0e400
1.9e-01
ID
17*-06
73*400
1.7e402
12*400
8.9*402
Mink
TCI
ID
3.0*403
•1.16402
4.7*401
NA
NA
5.6*403
1.4*402
NA
NA
3.4*401
18*402
13*402
1.3*401
NA
NA
3.4*-05
9.4*401
'12.403
NA
NA
Table 5-23
(continued)
River Rrver otter
otter TC TCI
ID
7.9e401
ID
14e400
33e4B
1.6*-01
1.5*402
3.8*400
ID
7.2*401
9.8*-01
7.7etOO
ID'
33*4)i
7.2*4)2
ID
93e-O7
24*400
5.6*401
"ID" "
19*402
ID
9.9*402
ID
1.7*401
NA
NA
1.9*403
4.7e401
NA
NA
1.2*401
9.6*401
ID
4.2e400
NA
NA
1.2e-05
3.0*401
7.06402
NA
NA
Bald
eagle TC
43*400
ID
JJD
34*401
4.9e-02
4.2e-02
ID
19e400
ID
ID
1.0*401
ID
1.3e401
1.0*400
6.16400
ID
1.2*-04
15*4)1
ID
8.9*400
ID
BaUeagk
TO
23e401
ID
ID
1.76402
NA
NA
ID
136401
NA
NA
5.0*401
ID
6.7*401
5:0*400
NA
NA
5.86-04
13*400
ID
NA
NA
Oiprej Osprej
TC TCI
3.2*400 4.06401
ID ID
ID ID
146401 3.06402
3.6e-02 NA
19*4)2 NA
to ID
10*400 16*401
ID NA
ID NA ,
736400 9.16401
ID ID
. 10*402 146403
I !M> |i 1 Q Tf 1 fifl
* *tWVA y*JV 1 \^&
43*400 NA
ID NA
5.76-05 7.1e-04
1.4e-01 1.8*400
ID ID
6.1*400 NA
tt) NA
Great blue
heron TC
3.66400
ID
ID
2364Q1
3.9e-02
18e-02
ID
12*400
ID
ID
8.1*400
ID
116402
8364)1
4.6*400
ID
6.U-05
1.76-01
ID
6.2*400
ID
Great blue
heron TQ
1.8*401
ID
ID
1.3*402
NA
NA
ID
1.1*401
NA
NA
4.0*401
ID
1.0*403
43*400
NA
NA
3.16-04
83*4)1
ID
NA •
NA
.(continued)
(A
o
W
tn
3
1
I
s*
90
-------
Table 5-23 (continued)
Constituent name
Acffnaphtiicnff
Aidnn
'• Antimooy
Anenic
Barium
Benz(a)anthracene (U-)
Benzo(a)pyrcne
Beryllium
Bis(2-ethylhexyl)phthalate
(also DEHP)
Burylbenzyl phthalate
Cadmium
Chkndane
Chromium VI
Chiysene
Copper
DDT
Di-n-octyl phthalate
Dieldrin
Diethyl phthalate
Dimethyl phthalate
Eodosulfan
Endrin
. Fluonuitheae
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
HexachKorocyclohexane,
gamma- (Liodane)
Hexachlorocycbpentadiene
Mallard
TC
ID
..,...„„ ..
m
lOfriOi
11*402
ID
ID
ID
ID
.i
. «n>
6.9*400
ID
mi '
ID
ID
7.2e-02
ID
1.8e-01
ID
ID
ID
l.le-01
ID
ID
ID
1.2e400
18e+00
ID
Mallard
TCI -
ID
7.2«-01
HA
NA
NA
ID
•ID
NA
ID
ID
NA
"" ro
" NA
ID
NA
14*400
ID
6.0e400
ID
ID
ID
3.6*400
ID
ID
ID
4.1*01
9Je*01
ID
Lesser
scanp
TC
ID
1.9*02
"""US
1.9e401
106402
ID
ID
ID
ID
ID
6.5*400
"ID
ID
ID
ID
6.2*02
ID
1.6*01
ID
ID
ID
9.3*02
fi>
ID
ID
1.2*400
16e400
ID
Lesser
scaup
TCI
ID
6.2e-01
NA
. .........
NA
ID
ID
NA
ID
ib
NA
.'"" ib
NA
ID
NA
116400
ID-
5.2*400
ID
ID
ID
3.1e*00
ID
ID
ID
3.96401
8.8ei01
ID
King- Klng-
Ihfaer Usher
TC TO
ID ID
l.Se-02 13e-01
'ID NA"
i.Se^Oi NA
1.9e*02 NA
ID ID
ID ID
ID NA
ID ID
ID ID
6.4*400 NA
" ID ID
ID NA
ID ID
ID NA
8.0e-02 1.06400
ID ID
1.6e-01 10e400
ID ID
ID ID
ID ID
l.Oe-01 1.3e+00
ID ID
ID ID
ID ID
1.2e400 1 Jc401
ISefOO 3.2C401
ID ID
Spotted
sandpiper
TC
ID
1.9e-02
'ID'"
1.8C401
1.8C402
ID
ID
ID
ID
ID
6.1*400
'."ID :
ib
ID
ID
7.1*02
ID
1.6*01
ID
ID
ID
8.6*02
ID
ID
ib'
l.le+OO
14*400
ID
Spotted
saadplper
TQ
ID
6.2*01
"""NA"
NA
NA
ID
ID
NA
ID
ID
NA
;H>
NA
ID
NA
14*400
ID
5.2*400
ID
ID
ID
19*400
ID
ID
ID
3.8*401
8.0e401
ID
t
Herring
full
TC
ID
3.1*02
'ib"""'"
3.6*401
3.1*402
- ID
ID
ID
ID
ID
.9.7*400
"~ ffi
ID
ID
ID
1.0*01
ID
2.6*01
ID
ID
Ib
1.5*01
ID
ID
ID
1.8*400
4.1e400
ID
Herring
gull
TCI
ID
3.8*01
NA"
"""NA
NA
ID
ID
NA
ID
ID
NA
ID
NA""""
ID
NA
1.3*400
ID
3.2*400
ID
tb
ID
1.9*400
ID
ID
ID
12*401
5.1*401
ID
Fhk Fbh
TC TQ
4.1*401 106402
1.1*400 5.66400
ffi NA
18«-02 NA
ID NA
10*01 1.06400
15*03 1.3*02
9.7*02 NA
16*403 1.3*404
7.7*404 3.8*405
11*01 NA
8.2*400 4.1*401
6^*03 NA
ID ID
ID NA
1.1*400 5.6*400
ID ID
1.8*400 8.9*400
1.1*402 5.4*402
3.4*404 1.7*405
33*02 1.7*01
13*400 6.5*400
6.3*400 3.1*401
1.1*402 5.4*402
4.9*403 15*404
406402 106403
17*02 3.8*bi
6.0*02 3.0*01
(continued)
t/i
b
w
en
U
n
o
q
&
o*
5
f
$
o"
-------
Constituent Bane
Hexachlorophene
Kepone
"Lead "
Mercury
Methoxychlor
Methyl parathion
Molybdenum
Nickel
Parathion
Pentachlorobenzene
Pentachlorophenol
PolycMorinated biphenyls
(Aroclor-1254)
Selenium
Silver
TCDD. 13.7,8-
Toxaphene
f richiorophenoxyacetk acid.
2,43-
Vanadium
Zinc
ID * Insufficient data.
NA - Not applicable,
Mallard
TC
ID
10*401
3.0*4)2
12*4)2
ID
1.7e400
ID
ID
6.2*400
ID
:»
,135*402
63*4)1
3.6*400
ID
4.7*4)5
1.4*4)1
ID
4.8*400
""""ID
.-
Mallard
TO
ID
6.6e4O2
NA
""NA
ID
5.6*401
NA
NA
11*402
ID
5.3*403
12*401
NA"
NA
1.6*4)3
4.8*400
10
NA
NA
•
Lesser
SCMp
TC
ID
1.9*401
£9*4)2
12*4)2
ID
1.6*400
ID
ID
5.9*400
ID
13*402
5.9*4)1
3.4*400
ID
4.7*4)5
1.2*4)1
ID
43*400
ID
•
Table
Lesser
scaup
TO
ID
63*402
"NA
" NA
ID
5.4*401
NA
NA
10*402
ID
5.1*403
10*401
NA"""
NA
1.6*4)3
4.1*400
ID
NA
NA
5-23 (continued)
King- King-
fisher fisher
TC TQ
ID ID
1.8*401 13*402
18*4)2 NA
""12*4)2 NA
ID ID
1.6*400 10*401
ID NA
ID NA
5.7*400 7.1*401
ID ID
13*402 1.9*403
5.8*4)1 7.3*400
3.3*400 NA
ID NA
4.4*4)5 53*4)4
1.2*4)1 13*400
ID tt>
43*400 NA
ID NA
Spotted
sandpiper
TC
ID
1.7*461
17*4)2
lic4J2
ID
13*400
ID
ID
5.4*400
ID
1.4*402
5.6*4)1
3.2*400
ID
4.1*4)5
1.1*4)1
ID
43*400
ID
Spotted
sandpiper
TO
ID
5.8*402
NA
NA :v
ID
5.0*401
NA
NA
1.8*402
ID
4.8*403
1.9*401
NA
NA
1.4*4)3
3.8*400
ID
NA
NA
Herring
gull
TC
ID
19*401
43*4)2
3.6e4)2
ID
14*400
DO
ID
9.1*400
ID
13*402
9.2*4)1
5.1*400
ID
7.2*4)5
11*4)1
ID
6.9*400
ID
Herring
gull
TO
ID
3.6*402
NA'"V
NA
ID
3.0*401
NA
NA
1.1*402
ID
19*403
1.2*401
NA
NA
9.0*4)4
16*400
ID
NA
NA
Fish
TC
ID
11*400
1,4*4)1
1 iisetdT
i:8e4)l
4.6*4)3
ID
1.3e4)l
1.6*4)2
5.7e404
1.0*401
1.6*400
4.4*4)1
ID
ID
1.1*400
18*400
ID
1.8*401
Fish
TCI
ID
1.0*401
NA
•; NA
9.2*4)1
13*4)2
NA
NA
8.2*4)2
18*405
5.1*401
8.2*400
NA
NA
ID
5.3*400
1.4*401
NA
NA
tst
b
1
M
en
Li
3
g.
r*
ions for Ecological Receptors
-------
5.0 EXPOSURE
5J Concentrations for Ecological Receptors
pro
(5-138)
For nonbioaccumulative constituents, the benchmark for fish/aquatic organisms (i.e., the FCV
or SCV) was adopted as the protective exposure concentration (see Table 4-4). For
hydrophobic nonpolar constituents that do not ionize,,the sediment community benchmark
calculated in Section 4 was used as the protective exposure concentration for benthic
organisms.
sediment benchmark • C. = /„ K FCV (also SCV)
(5-139)
where
sediment benchmark
FCV
SCV
benchmark for sediment community (mg/kg sediment)
protective concentration for sediment community (mg/kg
sediment)
fraction of organic carbon in sediment (unitless)
organic carbon/water partition coefficient (L/kg)
final chronic value (mg/L)
secondary chronic value. (mg/L)
Mammals and Birds. For bioaccumulative contaminants, protective exposure
concentrations for mammals and birds were backcalculated by dividing the appropriate TCI
by the BAFI as shown in Equation 5-138. TIw mallard, lesser scaup, and spotted sandpiper
were assumed to feed exclusively on benthic invertebrates and the invertebrate BAFI from
the .littoral food model was used to estimate protective exposure concentrations for these
receptors. .
For 2,3,7,8-TCDD the BSAF was used to estimate C^ for sediment As stated above,
the Agency has demonstrated that the BSAF provides a more reliable measure of
bioaccumulation potential for highly hydrophobic constituents. The relationship C^ for
sediments and BSAF is expressed:
" - C
"tnt Ji
BSAF
(5-140)
where
c
foe
BSAF
flipid
protective concentration in sediment (mg/kg sediment)
whole-body tissue concentration (mg/kg fish)
fraction organic carbon in sediment (unitless)
biota sediment accumulation factor (unitless)
fraction lipid in fish assumed in model (unitless).
August 1995
5-188
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
Protective exposure concentrations for nonbioaccumulative constituents were calculated
as described for the* limnetic ecosystem (Section 5.3.2.1.3). However, because the sandpiper
ingests between 7 and 30 percent of sediment in its diet (Beyer et at, 1994), Equation 5-115
was revised to include" sediment intake for this reflector. Rounded to one significant figure,
the central tendency value of 20 percent was selected to represent the fraction of sediment in
the sandpiper diet To calculate the protective surface water concentration for the sandpiper,
was adjusted to account for sediment ingestion (see Table 5-19):
C ' = benchmark x bw
'
In addition, the protective sediment concentration was calculated for die spotted
sandpiper since the sediment pathway may result in higher exposure than the food pathway
for chemicals that bioconcentrate weakly. To estimate C^ for sediments, the following
equation was used:
Cpro - x bw (5.142)
'sediment
where
Cpn, = protective sediment concentration (mg/kg sediment)
Isediment = dietary intake of sediment (kg/d)
benchmark = lexicological benchmark (mg/kg-d)
bw = body weight for the spotted sandpiper (kg).
\
•••*'>
Both the surface water and sediment concentrations were used to backcalculate acceptable
waste concentrations for the spotted sandpiper.
Aquatic Plants. The protective exposure concentrations for aquatic plants (algae or
vascular plants) are described in Section 4 on ecotoxicological benchmarks. Briefly, the
lowest effects concentration was selected to represent the aquatic plant community for effects
levels corresponding to roughly 20 percent (e.g., 20 percent decrease in cell number).
5.3.4 Terrestrial Ecosystem ,
Ecological receptors may be exposed to contaminants in soil through direct ingestion,
direct contact (for soil fauna and plants), or through the ingestion of contaminated prey or
vegetation. For plants, the ecotoxicoldfical benchmarks shown in Table 4-4 were used
directly as the protective exposure concentrations since the route of exposure was assumed to
be direct contact with contaminated soil. The benchmarks for the soil community developed
in Section 4 were considered to be protective exposure concentrations for soil fauna. The
route of exposure to organisms in a soil community was assumed to be through both the
ingestion of, and direct contact with, contaminated soil
For mammals and birds, exposures to bioaccumulative chemicals were estimated based
on methods from the Assessment of Risks from Exposure of Humans, Terrestrial and Avian
August 1995 5-189
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
Wildlife, and Aquatic Life to Dioxins and Fwans from Disposal and Use of Sludge from
Bleached Kraft and'Sulfite Pulp arid Paper Mills (U.S. EPA, 1990b) and the Technical
Support Document for the Determination of the Need to Regulate Pulp and Paper Mill Sludge
Landfills and Surf ace-Impoundments (Abt Associates, Inc., 1991). The terrestrial food web
developed in those documents was modified to represent a generic terrestrial ecosystem by
including additional representative species. However, unlike the limnetic food chain and
littoral food web, the terrestrial food web was not based on a trophic level approach to
estimating wildlife exposures. Instead, the terrestrial food web was defined in terms of
dietary preference for other organisms within the food web. For example; the diet for the
raccoon—defined in terms of percentage consumed of vertebrates (e.g., small mammals),
earthworms, invertebrates .(excluding earthworms), and plants—does not explicitly account for
the bioaccumulation through specific trophic level interactions (i.e., soil microorganisms -»
earthworms -» small mammals -» raccoon). Nevertheless, the methodology used to evaluate
dioxin risks was considered to be the best available approach to evaluating chemicals that
may bioaccumulate in terrestrial ecosystems. Exposures to nonbioaccumulative chemicals
were estimated using the same equation used to evaluate bioaccumulative chemicals although
exposure through ingestion of contaminated soil typically dominated.
The following sections describe the methods used to backcalculate the protective
exposure concentration for representative species of wildlife—plants, soil fauna, mammals,
and birds—for the generic terrestrial ecosystem.
5.3.4.1 Plants
As stated in Section 4 on endpoints, toxicological benchmarks were identified for
adverse effects believed to impact plants at the population level (e.g., growth, yield). The
universe of plants considered was limited to terrestrial vascular plants including trees, forage
grasses, and food crops. Depending on the species of plant, the constituent of concern, and
the exposure source, contaminants can enter plant tissues through one or more routes: soil-to-
root uptake, air-to-leaf uptake during transpiration, and soil-to-leaf uptake as a result of the
adhesion of soil particles to the aboveground portions of plants. Since some benchmarks for
plants come directly from empirical studies, it was assumed that the benchmark was
indicative of the route(s) of exposure relevant to phytotoxicity from contaminated soil.
However, a methodology to estimate benchmarks for the air-to-plant exposure route was not
identified for the constituents of concern. In addition, the plant benchmarks do not consider
the effects of direct deposition of contssainants (e.g., stomatal plugs). As with the
benchmarks for aquatic plants, the benchmarks for terrestrial plants presented in Section 4
were used as the protective exposure concentrations for plants exposed to contaminants in •
soil It should be emphasized that the C^ for plants is valid only for soil-to-plant exposures.
5.3.4.2 Soil Fauna
The exposure scenario developed for the soil community was less complex than that
developed for other wildlife communities. All soil organisms were considered to be exposed
to the constituents present in the soil at the concentrations determined by each of the relevant
August 1995 5-190
-------
5.0 EXPOSURE . 5.3 Concentrations for Ecological Receptors
exposure pathways. Because members of the soil community live in intimate contact with
soil, it was assumed that the major route of exposure was from direct contact with soil rather
than either inhalation or ingestion. In earthworms and nematodes, for example, the majority
of the exposure is thought to occur as a result of bioconcentration through direct contact with
the soil pore water, rather than through ingestion of contaminated organic matter (van Gestel
and van Straalen, 1994). For species with a chitinous integument (e.g., a beetle) the actual
amount of contaminant absorbed by direct contact may be lower than for those species with a
lipid-like integument (e.g., earthworm). Because direct dermal uptake of contaminants may
be less important when compared to oral uptake for organisms with a chitinous exoskeleton,
studies that exposed soil organisms via the dietary route were also used in the development of
the soil community benchmarks. .
' As discussed in Section 3, the soil community was represented by selecting a set of
eight species from a range of taxa. Based on six metrics for determining ecological
significance (e.g., biomass, size). The species selected for benchmark development are
members of the following taxonomic groups: (1) Nematoda; (2) Acarina (i,e., Crypto-
stigmata, Prostigmata, Mesostigmata, or Metastigmata); (3) Collembola; (4) Plesiopora or
Opisthopora, preferably one from each of the families Enchytraeidea and Lumbricidae; (5)
Diptera, Coleoptara, Isopoda, Chilopoda, or Diplopoda; and (6) Stylommatophora.
The method used to calculate the soil community benchmarks follows the Refined
Effects Assessment Procedure developed.by.the Dutch National Institute of Public Health and
Environmental Protection (RJVM). This procedure was used to calculate a hazardous
concentration for 5 percent of the species in the community (HC5) with two degrees of
confidence using the mean and standard deviation of laboratory-derived NOEC data for
representative species in the soil community. The HC5 was calculated with two degrees of
confidence, 95 and 50 percent At concentrations equal to or less than the HC5, 95 percent of
the species in the community were assumed to be protected. The extrapolation theory for this
method was developed by Kooijman (1987) and refined by van Straalen and Denneman
(1989) and Aldenberg and Slob (1993). Conceptually, it is similar to the method used to
derive AWQC, with the major exception being that it assumes a logistic distribution of
species sensitivities instead of a triangular.
To illustrate the benchmark calculations for the soil community, cadmium was chosen
as an example.
Using the data in Table 5-24, Equation 4-9 from Section 4.3.4 is written as
w? -1.81 -1.868
for 50 percent confidence and as
HC595 =exd2.047 -3.93 -1.868 =0.005(ms/*g) (5-144)
August 1995 5-191
-------
5.0 EXPOSURE 5.3 Concentrations for Ecological Receptors
Table 5-24. Cadmium Toxicity Data for Representative Soil Species
Species
PUuynothrus pcltifer
Orchesella cincta
Dehdrobaena rubida
Lumbricus rubellus
Porcettio scaber
Helix aspersa
Taxon
Acarina
Collembola
Oligochaeta
Oligochaeta
Isopoda
Gastropoda
NOEC
(mg/kg)
0.97
18.7
15.4
13.5
3.30. 0.75
3.63
In(NOEC)
(mg/kg)
-0.03
2.93
3.04
2.60
0.45
1.29
Data from van de Meent et al. (1990).
for 95 percent confidence. This benchmark was categorized as provisional because data were
obtained for only six of the eight required species. The parameter values for Equations 5-143
and 5-144 are summarized in Table 5-25.
The HC5 with 95 and 50 percent confidence represent the confidence range for this
benchmark. The HC5 with 95 percent confidence was assumed to be protective of 95 percent
of the species in the community 95 percent of the time, while the HC5 with 50 percent
confidence was assumed to be the best estimate of the 95 percent species protection level.
Although the HC5 was calculated at both levels of confidence, only the 50 percent confidence
value was used for determining ecological exit criteria.
5.3.4.3 Exposures to Mammals and Birds
The generic terrestrial food web included 12 representative species of birds and
mammals with five categories of expos$j£e vehicles: soil, plants, earthworms, other
invertebrates (e.g., insects), and vertebrates. The exposure scenario for this food web includes
direct and indirect ingestion of contaminated soil as well as the consumption of contaminated
vegetation or prey species. Exposure to contaminated water was not evaluated for terrestrial
mammals and birds because most species rely on a variety of sources for drinking water,
including seasonal ponds, puddles, and, for smaller species, dew. Given the variety of
drinking water sources, it appears unlikely that terrestrial animals would be more exposed
than aquatic animals that depend on a single drinking water source. Therefore, the drinking
water pathway was limited to the generic aquatic ecosystem.
August 1995 5-192
-------
5.0 EXPOSURE 5.3 Concentrations for Ecological Receptors
Table 5-25. Summary of Calculations for RIVM Methodology
Parameter
Percentage of species not protected
Number of species-specific means
Mean of ln(NOEQ
Standard deviation of ln(NOEQ
Distribution factor, for m=6 with 50 percent confidence
Distribution factor, for m=6 with 95 percent confidence
The 50 .percent confidence interval for the HC5
The 95 percent confidence interval for the HC5
Symbol
P
m
*m
S«
d«
d»
HC550
HC5*
Value
5
6
2.047
1.868
1.81
3.93
0.263
0.005
Exposures to terrestrial mammals and H^ds were estimated using the approach
developed by the U.S. EPA (1990b, 1991f) to cvalute the ecological risks associated with
dioxin-containing sludges. The underlying principle behind this methodology is that the
exposure to consumers in the upper trophic levels is a function of: (1) daily intake of food,
(2) the dietary preferences of the receptors, and (3) bioaccumulation (or bioconcentration) of
contaminants in food items*
The algorithm used to calculate the protective exposure concentration (C^) for
representative species in the terrestrial food web is presented below, with food items limited
to four categories: vertebrates, invertebrates, earthworms, and plants (Abt Associates, Inc.,
1993).
£, benchmark x bwi (5-145)
pro * *E (BCFj x Fij x ABij)
where
j • '
C— = protective exposure concentration (mg/kg soil)
bwi = body weight for species i (kg)
li. = total daily food intake of species i (kg/d)
B AFj =. bioaccumulation factor in food item j (assumed unitless)
Fij = fraction of species i's diet consisting of food item j (unitless)
August 1995 5-193
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
ABij = absorption of chemical in the gut of species i from food item j (all
" weights assumed to be wet weight).
The absorption (ABij) is generally assumed to be equivalent between the test species' diet and
wildlife diet and, therefore, the absorption term does not impact the calculations of protective
concentrations. It should be noted that this equation is applicable to both bioaccumulative
and nonbioaccumulative constituents. For nonbioaccumulative constituents. Equation 5-145
may be simplified as:
~ benchmark x bwi
where fMil is simply the fraction of the diet that consists of soil.
In addition to Equation 5-146, Equation 5-145 can be rewritten to solve for the
protective exposure concentration in plants (instead of soil) for the uptake and deposition of
airborne constituents onto plants (e.g., air-to-leaf transfer) and subsequent ingestion by
herbivores. Revising Equation 5-145 to solve for the concentration in plants results in:
CDro plants = benchmark x ** (5-147)
pro HxFij
where li is the quantity of plants ingested by 'he animal per day (kg DW/day) and Fij is the
fraction of plants consumed in the diet This equation applies only to herbivores whose diet
consists of close to 100 percent plants, and is limited to pathways TER IV and V shown in
Table 1-5 for the meadow vole, white-tailed deer, northern bobwhite, and eastern cottontail.
The protective soil and plant concentrations are presented below in Table 5-26 for
ecological receptors in the terrestrial ecosystem.
5.3.5 Ecological Inputs
The key inputs in estimating ecological exposures can be broken down into three main
categories: (1) physical characteristics of ecological receptors (e.g., body weight, food
intake), (2) exposure inputs for ecological receptors (e.g., hunting range; dietary fractions),
and (3) chemical-specific inputs on b&sccumulation, bioconcentration, and biomagnification
(i.e., biological uptake). Inputs used to determine protective exposure concentrations for
ecological receptors are discussed below for the generic freshwater and terrestrial ecosystems,
respectively.
August 1995 5-194
-------
Table 5-26. Protective
CoasUtueat Mine
Acenaphthene
AUrin
Antimony
Anenic
Barium
Benz(»>anthr»c*n« (1,2-j
Benzo(a)pyrene
Beryllium
Bii(2-ethyuS*x)d)phthtUle (alto BEHP)
Butylbenzyl phthalale
Cadmium
Chlordane
Chromium VI
Chrytene
Copper
DDT
Di-n-octyl phmalate
Dieldrin
Diethyl phthalale
Dimethyl phthalai*
Endotulfan
Endrin
Fiuorainihene
Heptachlor
Heptachlor epoxide
Hexachlorobenzene
Exposure
(all
Meadow
vote
ID
5.1*401
3.3*401
1.2*403
ID
tt)
18*402
ID
14*404
9.8*403
15*402
9.6*402
3.6*402
ID
4.9*402
4.0*402
D
17*400
ID
ID
5.8*401
"\ 9flf\\
JtlHrTvl
ff)
4.9*401
ff)
3.4*402
Concentrations for Ecological Receptors in
units in mg/kg soil or mg/kg on plant)
Meadow vote Eastern
(plant) cottontail
.ID
3.8e-01
8.2*-01
19*401
ID
ID"
3.1*400
ID
11*402
1.1*403
6.16400
1.4*401
ft ft^. j/V\
O>OV1\A/
ID
1.2*401
14*400
ID
7.8*-02
ID
ID
12*401
13*400
ff)
14*400
JD
8.7*400
ID.
. 8.7*401
11*401
7.2*402
ID •
ID
4.6*402
ID
1.8*405
1.6*404
1.5*402
'I 13*403
14*402
ID
3.1*402
9.9*402
ID
4.1*400
ID
ID
9.8*401
63*401
ff) '""'
7.6*401
ID
53*402
Eastern
cottontail
(plant)
ID
6.0e-01
1.4*400
4.6*401
n>
m
5.2*400
ID
3.5*402
1.8*403
9.6*400
12e401
13*401
ID
10*401
3.9*400
ID
1.2*-01
ID
ID
3.8*401
23*400
KB '"
3.8*400
ID
1.4*401
White-
tailed deer
ID
9.6e401
7.7e401
15*403
ID
"ID"""""
5.2*402
"ID"
10*405
1.8*404
5.2*402
1.7e403
7.5*402
ID
I.le403
1.1*403
ID
3.9*400
ID
ID
9.9*401
7.06401
"'""ff)"
8.66401
ID
5.8*402
the Terrestrial Ecosystem
White-
tailed deer
(plant)
ff>
636-01
136400
5.0*401
ID
" tt) ""
c o^ tJftf\
J*O*TXWJ
ID
3.9*402
106403
1.00401
14*401
13*401
ID
126401
4.2*400
0)
1J*-01
ID
0)
3.76401
2176400
m
4,2*400
tt)
136401
Northern
bobwhite
ID
1.16401
ID
1.26403
1.3*4O4
ID
ID
ID
ID
ID
5.0e402
ID
ID
ID
ID
14*401
ID
4.06401
ID
ID
ff)
136401
tt)
ID
tt)
3.16402
Northern
bobwhite
(plant)
ff)
1.3e-01
ID
1.3*402
1.4*403
ff)
ID
ID
ID
ID
5.3e401
ID
ID
ID
ID
4.46-01
ID
1.26400
ID
ID
ID
5.9*-01
ff) "
ff>
Q>
8.16400
Short-
tailed
ID
. 14*400
5.8*401
10*403
ID
tt)
4.6*4O1
n>
1.6*402
5.1*404
4.1*402
33e401
6.4*402
ID
836402
1.4*401
ID
4.26400
ID
ID
3.2*402
l.Oe+02
ID"
1.76402
ID
4.7e-03
. . ' (continued)
en
O
M
in
O
I
i
5'
1
90
1
-------
Table 5-26 (continued)
Constituent name
tiexaciuorocycioaexane, gwnnw- lunam
Hexachlorocyclopentadiene
Hexachlorophene
Kepone
Lead
Mercury
Methoxychlor
Methyl pumthion
Molybdenum
Nickel ,f
Parathion
Pentachlorobenzene
Pentachlorophenol
Poiychloniiated biphenyU (Arocibr-1254)
Selenium
Silver
tCDD. 13.7;}-
foxapbene
f ricntoropheaoxyacetk acid, 2,44-
Vanadium
Zinc
Meadow
vote
>) ID
4.5*403
1.0*403
44*401
1.0*4)1
7.9*401
' 5.0*403
14*401
1.9*401
1.0*404
1.4*401
5.3*402
5.0*402
1.9*401
9.0*400
ID
1.0*4)4
1.6*402
3.0*402
1.0*402
3.8*404
Meadow vote
(pint)
ID
24*402
8.7*400
4.4*400
14*4)2
1.9*400
4.6*402
13*401
4.7*4)1
15*402
3.3*400
13*401
12*401
1.1*400
12*4)1
ID
19*4)6
8.2*400
1.8*402
24*400
9.2*402
Eastern.
cottontail
D
7.3*403
7.1*403
7.1*401
1.7*4)1
5.0*401
8.1*403
13*401
1.2*401
6.1*403
11*401
8.5*402
V.8*402
1.9*402
iTetOO
ID
14e4)3
17*402
4.8*402
64*401
14*404
Eastern
cottontail
(plant)
ID
4.2*402
1.4*401
7.0*400
4.2*4)2
3.2*400
7.7*402
11*401
8.0*4)1
3.9*402
5.2*400
3.9*401
3.6*401
1.7*400
3.7*4)1
ID
5.0*4)6
1.4*401
3.0*402
4.2*400
14*403
White-
tailed deer
ID
8.2*403
8.1*403
7.9*401
1.8*4)1
1.7*402
9.2*403
16*401
4.4*401
11*404
14*401
9.7e402
8,6*402
•12*402""
1.9*401
ID
1.6*4)3
3.0*402
54*402
13*402
84*404
White-
tailed deer
(plant)
ID
4.6*402
1.6*401
7.7*400
4.2*4)2
34*400
8.6*402
12*401
8.8*4)1
4.2*402
5.8*400
43*401
3.8*401
""1:9*406""."
3.8*4)1
ID
5.4*4)6
14*401
33*402
4.6*400
1.7*403
Northern
bobwhlte
6.3*401
ID
ID "
1.3*403
7.9*4)1
1.4*400
ID
13*401
ID
ID
1.7*402
ID
13*404
1.2*401
12*402
ID
1.6*4)3
1.8*401
H)
3.0*402
ID
Northern
bobwhlte
(ptant)
1.8*401
fir
ID
1.3*402
1.9*4)1
14*4)1
ID
1.1*401
ID
ID
4.1*401
ID
1.0*403
4.1*400
13*401
ID
3.1*4)4
8.8*4)1
ID
3.2*401
IP
Short-
taOed
•brew
ID
1.8*404
7.0*400
12*402
1.7*4)1
1.4*402
15*404
7.9*401
34*401
1.7*404
7.1*401
3.1*4)2
8.2*4)2
1.2*4)1
14*401
ID
6.6*4)7
5.9*402
1.6*402
1.8*402
6.6*404
\ (continued)
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
-------
' -
Constituent name
Acenaphthene :
Aldrin
Antimony
Arsenic
Barium
B«^a>nthncene (12-)
Benzo(a)pyiene
Beryllium
Bis(2*lhylhexyl)phthalate (also
DEHP)
Butylbenzyl phthalale . |
Cadmium
.Chloidane
Chromium VI
Chiysene
Copper
DDT
JDi-iHKtyi phthalate
Dieldrin
Diethyl phthalate
Dimethyl phlialate
EndosuUan
Endnn
Fluoranthene
Hepuchlor
Heptachlor epoxide
Hexachlorobenzene
Deer mouse
ED
3.9e*01
8.2e401
18*403
ib""""""
m
5.4*402
ib
19*403
43*404
5.8*402
15*403
8.9*402
' H>
i.fc403
Ue402
ID
1.0*401
ID
to
17*402
1.7*402
ID
11*402
ID
1.3*403
Red fox
ED
1.2*-01
3.0*401
1.0*403
ED"
ib"
1.9*402
ib""":'
6.6*402
14*405
11*4O2
19*401
3.2*402
'ib'
43e+02
3.2*400
to
33e-03
"ib"
ED
13*403
4.7*402
. ED
7.6*402
ED
1.8*403
Table 5-26
Raccoon
ED
5.9e-01
1.7*401
5.4*402
ED
ED"
1.6*402
ED"""
c ^.^ «rto
J» 1 VTV*
63*404
1.1*402
1.3*402
1.7e402
JD
3.8*402
1.0*401
to
1.8*-02
ib'
ED
3.6*402
1.8*402
ED
15*402
ED
1.0*403
(continued)
Red-tailed
hawk
to
1.90-02
ED
5.5*403
~$35*6*
Kb
'ib"
ID
to
n>
1.9*403
ED
- ED
ib
'ib'""
ZSe-01
ID
12e-02
"ib"
to
ID
1.6*402
ID
ID
ID
9.1*402
American
kestrel
to
lle-02
to
3.2*403
33*404
'ib'
ED
"ib""
ib
ED
1.1*403
ED
ED
'ib"
ib
19e-01
ED
13*-O2
ID
ID
to
8.8*401
ID
ED
ED
5.3*402
American
room
ED
18*-01
ED
8.9*402
92e403
'ib' """"
ED
ID
"" ib
ED
3.1*402
ED
ED
ib
ED
3.9c-01
ED
1^*401
".' ib"
ID
ID
7.1*400
ED
ID
ID
9.2*401
American
•MI mill ii nil
WOODCOCK
ED
5.6e-O2
ED
I.le402
1.1*403
ib
ED
""ib" "
""ib"
ED
3.9*401
n>
ED
ED" ;""
ED"
ZOe-01
""ED"
5.2*400
""ib"
ED
ED
33*400
ED
ED
ED
16e-04
SoU fauna
ED
ED
ED
10 . i
ED
"""ib"
ED
ED
ED
ED
6.9e-01.
ED
ED
ib' •
13*400
ID
ED
ED
ED
ED
ED
ib
ED
ED
ED
ED
Plants
ID
ED
5.0*400
1.0*401
5.0*402
"ib
ED
1.0*401
""'ib'
ED
1.3*400
ED
1.0*4)1
""ib
1.0*402
ID
ID
ED
ED
ED
ED
ib
to
ID
ED
ID '
(continued)
t/i
f
I
90
-------
Constituent Dime
Hexachlorocyciohexane, gamma-
(Lindane)
Hexachlorocyclopentadiene
Hexachlorophene
Kcpooe
Lead
Merciny
Melhoxychlor
Methyl parathion
Molybdenum
Nickel |
Parathion
Pentachlorobenzene
Pentachlorophenol
PoiychJoriniited biphenyls
(Aroclor-1254)
Selenium
Silver
TCDD. 13.7.8-
Toxaphene
Trichloropbeooxyaceuc acid, 14,5-
Vanadium
Zinc
Deer noose
ID
100404
IJ0402
100402
4.lc-01
100402
130404
6.50401
5.00401
140404
5.90401
130403
110403
120400
110401
ID
1.2e-05
7.3*402
1.40403
150402
9.30404
ID - Imufficient data.
Note: The protective exposure ooncentiatiom (in mg/kg
Red fox
ID
8.2e«04
3.0ei01
1.0e^03
9.1e-02
1.0e400
1.2e+07
3.7et02
1.8e+01
8.7»
1.4*403
n>
AmericM
kestrel
7J*404
ID .
fi>
8.4*404
3.2*-01
3.7*400
ID
3.1C40S
ID
ID
1.3*405
ID
3.10403
"•"3":5'£oi
5.80402
ED
l.Se-05
1.10400
ED
8.1*402
ID
American
robin
3.90401
ID
ID
8.00402
15*-01
1.00400
ID
7.90400
ID
ID
1.00402
n>
1.20404
1.404)1
1.60402
ID
10e-05
8.90400
ID
120402
o>
American
^i nil *-
WOOOCOCK
3.90400
ID
ID
8.90402
l.le-01
1.20-01
ID
1.00401
ID
ID
1.30402
ED
130-01
•"18£02
100401
ED
4.00-06
5,30400
ED '
190401
ID
Soil fauna
ID
ID
ED
DD
150-01
9.4*4)1
ED
ID
ID
ID
ED
ED
ID
ID
ED
ED
ED
ED
ED
n>
3.60-02
Plaits
ED
ED
' £D
ID
5.00401
3.00-01
ED
ED
ID
3.00401
ED
ID
ED
4.00401
1.0*400
10*400
ED
ED
ID
100400
1.00401
•oil community and tencstrial plants are presented in Table 4-5.
Ul
0
M
Ul
U
9
H
1
S"
1
»
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
5.3.5.1 Generic Freshwater Ecosystem
5J.5.1.1 Physical Characteristics
Body weights and food intake rates were identified in the Wildlife Exposure Factors
Handbook (U.S. EPA, 1993g) and selected to be broadly consistent with the Great Lakes
Water Quality Initiative for the Protection of Wildlife—(Proposed) (U.S. EPA, 1993a). In
many cases, the number of available body weights for different life stages (e.g., incubation,
courtship) required some interpretation in order to select .an appropriate body weight Where
multiple weights were available for similar life stages, the geometric mean was calculated
and, when possible, body weights were estimated for males, females, and a geometric mean
across sexes. However, it should be pointed out that the variability in body weight data is
unlikely to be a significant source of uncertainty, regardless of which value is used. Body .
weights and lipid fractions for aquatic organisms contained in the bioaccumuladon models
were taken directly from the input tables that support those models (Thomann, 1989;
Thomann et al., 1992). The key physical characteristic of the sediment (i.e., fraction of
organic carbon = O.OS) was based on the soil type that corresponded to high-end
concentrations in the sediment and was within the recommended range found in the
Addendum: Methodology for Assessing Health Risks Associated with Indirect Exposure to
Combustor Emissions (U.S. EPA, 1993a).
5J.5.1.2 Exposure Inputs
The dietary fractions assumed for fish r ^estion by birds and mammals were established
to be consistent with assumptions in the Great Lakes Wajter Quality Initiative Criteria
Documents for the Protection of Wildlife—(Proposed) (U.S. EPA, 1993e) for the mink, otter,
eagle, kingfisher, and osprey. Dietary fractions for other representative species were
estimated using the Wildlife Exposure Factors Handbook (U.S. EPA, 1993g). As in the Great
Lakes Initiative, hunting ranges were assumed to be limited to the contaminated waterbody
and the diet was assumed to consist exclusively of fish. For the mallard, lesser scaup, and
spotted sandpiper, the diet was assumed to consist entirely of aquatic invertebrates. Although
the mallard feeds primarily on seeds, leaf litter, rootlets, tubers, etc., in the winter, the spring
diet switches almost completely to invertebrates for females as they try to obtain sufficient
protein for egg production and prebasic molt Despite the comprehensive research on dietary
fractions contained in the Handbook* the data were highly variable depending on the season,
species, and study author. For example, some authors reported dietary tractions as a function
of food mass in die gut and others reported the incidence of certain food types identified for a
particular species. Whenever possible, the spring and/or summer diet was selected to
represent sensitive reproductive stages for wildlife.
5.3.5.1.3 Biological Uptake
The models used for the limnetic food chain and littoral food web have limitations with
respect to log K^. Specifically, the models (Thomann, 1989) do not adequately predict
August 1995 5-199
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
BAFts for chemicals with log K^ > -6.5 due to (1) the behavior of chemical assimilation
efficiency (a) as a function of Kow, (2) the behavior of the BCFf for phytoplankton in
trophic level 1, and (3) changes in the absorption dynamics (and chemical assimilation) across
the gastrointestinal tract for highly lipophilic constituents (i.e., constituents with log Kow »
6.5). In addition to Kow-based limitations, the models are not appropriate for certain classes
of compounds (e.g., inorganics) and, in particular, do not consider decreases in accumulation
due to metabolism demonstrated for chemical families such as PAHs. As a result, measured
bioaccumulation factors were compared to predicted bioaccumulation factors to determine: (1)
if there was a descrepancy that could not be explained by metabolism or hydrophobicity and
(2) if the measured value could be used to represent bioaccumulation at one or more trophic
levels. Although a default BAFI of 1000 was used for PAHs lacking measured data
(Stephan, 1993), decisions on BAFIs, BCFIs, etc., were made with respect to chemical
family and, for some constituents (e.g., TCDD and mercury), on a chemical-specific basis.
The BAFI (and/or BCFf) derivation for these "special cases" is included with the
toxicological profiles (Appendix B) on the constituents of ecological concern.
5J.5.2 Generic Terrestrial Ecosystem
5.3.5.2,1 Physical Characteristics
For mammals and birds, the body weights, daily food and soil intake, and dietary
fractions shown in Table 5-27 and were based on (1) the Wildlife Exposure Factors
Handbook (U.S. EPA, 1993g), (2) the Mammalian Species Series (Smith, 1991), (3)
allometric equations developed in Recommenuutions for and Documentation of Biological
Values for Use in Risk Assessment (U.S. EPA, 19881), or (4) allometric equations presented in
Toxicological Benchmarks for Wildlife: 1994 Revision (Opresko et al., 1994). For plants and
soil fauna, key physical characteristics of soil (e.g., percent clay, pH, f^) were identified
when reported; however, they were not needed to calculate protective exposure concentrations
(or toxicological benchmarks).* .
5.3.5.2.2 Exposure Inputs .
The dietary fraction of various food items was one of the most difficult parameters to
estimate. Not only is there variability in dietary habits across regions, sexes, and species,
there is also seasonal variability in food items, sometimes over an order of magnitude. For
example, the diet of the American robi&.(eastern United States) consists of approximately 90
percent invertebrates in the spring and about 8 percent in the fall (Wheelwright, 1986). Since
the primary toxicological effects of interest were developmental and reproductive effects,
spring and/or summer diets were used when available. If regional spring diets were
identified, the diet consisting of a higher percentage of earthworms was chosen since
•If reported, the foe value (organics) and the percent clay value (metals) from a soil study were used to
normalize effects levels to a standard soil. However, if no foe or percent clay values were reported, the effects
levels were used unadjusted.
August 1995 5-200
-------
5.0 EXPOSURE
52 Concentrations for Human Receptors
Table 5-27.- Exposure Inputs for Representative Species in the
Generic Terrestrial Ecosystem
Representative
species
Short-tailed shrew
female
male
both
Deer mouse
female
male
both
Meadow vole
female
male
both
• Eastern cottontail
female
male
both
Red fox
female
male
both
Raccoon
female
male
both
White-tailed deer
female
male
both
Red-tailed hawk
female
male
both
American kestrel
female
male
both
Northern bobwhite
female
male
both
American robin
female
male
both
American woodcock
female
male
both
Body weight
(kg)
0.017
0.017
0.017
0.019
0.020
0.019
0X09
0.043
0.033
122
1.13
122
4X4
5.04
4,54...;
4.71
622
5.62
76X10
110.00
85.00
120
1.06
1.13
0.13
0.11
0.12
0.17
0.16
0.17 __ .
0.082
0.082
0.081
020
0.15
0.17
Soil intake
% of diet
1
1
1
2
2
2
2.4
2.4
2.4
63
63
63
2.8
2.8
2.8
9.4
9.4
9.4
2
2
2
93
93
93
•*»
10.4
. 10.4
10.4
a = Food consumption rate for dry matter ingestkra is based
b = Food consumption rate for dry matter ingestioh is based
c = Reported food intake rate was not gender specific.
kg/d Food intake (kg/d)
9.4e-05
9Je-05
92e-05
7.1e-05
8.8e-05
7.4e-05
3.0e-04
33e-04
2.6e-04
6.4e-03
6.0e-03
6.4e-03
8.1e-03
l.Oe-02
1.2c-02
23e-02
2.9C-02
2.7e-02
4.1e-02
53e-02
4.4C-02
13e-OS
l.le-03
l.le-03
3.7e-04
3.4e-04
3.6e-04
12e-03
12e-03
13e-03
9.9e-04
9.9e-04
9.8e-04
1.66-02
126-02
13e-02
on the equation F
on the equation F
0.0094
0.0095
0.0092
OXO35
OXKM4
0.0037
0X113
0X114
0.011
0.10
0.10
0.10
029
036
043
025
031
028
2X*
167
221
0.13
0.11
0.11
0X07
OXO4
OXO6
0X113
0X113
OX»14 .
0.10
0.10
0.10
0.16
0.12
0.13
Sprtsuj/suBmer diet
COOMUBptkM
(% vol)
a
a
a
b
b
b
a
a
a •
c
•c
c
c
c
c
c
« 0.577
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
earthworms have been shown to bioconcentrate a variety of chemicals (particularly
hydrophobia chemicals) over other invertebrates (e.g., insects) or small mammals.
5.3.5.2.3 Biological Uptake
The bioaccumulation factors are specific to the constituent and food category and were
the most resource-intensive data evaluated for the terrestrial ecosytem. Unlike the BAFs for
freshwater ecosystems, the BAFs for terrestrial organisms are frequently not examined oh a
lipid basis and, in fact, the algorithm presented above does not require a lipid-based BAF
(i.e., BAFf). For plants, the BCF values for inorganics were identified in the Technical
Support Document for Land Application of Sewage Sludge (U.S. EPA, 1992e) or calculated
using regression equations developed by Baes et al. (1984). The Technical Support Document
contains data on uptake-response concentrations of metals in a variety of plants, including
forage grasses. Where multiple values were available for a given plant category (e.g., leafy
vegetables), the geometric mean was calculated and used as the chemical-specific BCF for
that plant category. For example, the BCF for forage grasses was chosen to represent the
typical plant composition eaten by deer and the eastern cottontail. Models similar to the
Thomann approach to estimating BAFs for various trophic levels were not identified for use
in this analysis. Therefore, whole-body BAFs were identified in the open literature and the
geometric mean of reported values was calculated for the other three major food categories:
vertebrates, earthworms, and invertebrates (excluding earthworms). As a default for
hydrophobic organic chemicals lacking data, the BAFs for vertebrates, invertebrates, and
earthworms were derived by using TCDD as the standard and adjusting the biological uptake
factors for TCDD presented in Revision of Assessment of Risks to Terrestrial Wildlife from
TCDD and TCDF in Pulp and Paper Sludge (Abt Associates, Inc., 1993) by biotransfer ratios
for soil to meat For example, the BAF for vertebrates (e.g., field mice) for a chemical
lacking specific vertebrate BAF data was estimated by multiplying the high-end BAF
vertebrates value of 1.3 for TCDD by the ratio of the beef biotransfer factors (BBF) for the
unknown chemical and TCDD (i.e., chemical BBF/TCDD BBF). Assuming that the
bioaccumulation of hydrophobic organics is a function of the lipid content, the ratio of soil-
to-beef transfer factors should provide a reasonable approximation of the potential of
chemicals to bioaccumulate. Although this method lacks the elegance of the equations
developed by Thomann (1989), it is a useful default approach to filling data gaps on
terrestrial bioaccumulation. Further, although algorithms have not been, developed to predict
wildlife BAFs or BCFs from Kow, the body of evidence continues to grow suggesting that
bioaccumulation in terrestrial biota is Issgely a function of Kow (Belfroid et al., 1993; Winter
and Streit, 1992; Connell and Markwell, 1990). The BCF and BAF values for constituents of
concern for vertebrates, invertebrates, and earthworms are shown below in Table 5-28.
>
5.3.6 Uncertainties and Issues of Concern
The uncertainties associated with estimating exposures for ecological receptors in
generic ecosystems are considerable compared to the uncertainties in benchmark development
or endpoint selection. Major sources of uncertainty include both modeling uncertainty and
knowledge uncertainty. The models used to estimate bioaccumulation factors have been
August 1995 5-202
-------
Table 5-28.
. Constituent name.
Acenaphthene
Aldrin
Antimony
Arsenic
Barium
Ben^a)aitthracene
(U-)
Benzo(a)pyrene
Beryllium
Bis(2-«lhylhexyl>-
phthalate (also
DEHP)
Butylbenzyl
phthalate
Cadmium .
Chkudane
Chromium VI
Chrysene
Copper
DDT
Di-n-octyl phthalate
"Worms
8.6*04
3.0e-01
ID
ID
ID
4.5*02
1.3e-01
ID
2.7e+00
2.4e-03
3.5e+00
8.5*01
ID
5.2*02
n>
16*01
3.0e+00
Bioaccumulation Factors and Bioconcentration
Factors for
Prey in the Terrestrial
Reference Invertebrates . Reference Vertebrates Reference
cak l.le-04 cak
cak 3.7*02 cak
ID
ID
ID
caic 5.6*03 cak
cak 1.6e^02 cak
$ ID
jp _.._,. .... , „, , ,
cak 3.3e-01 cak
cak 3.0e-04 cak
Davies. 1983; Hetoike, 1979 ID
Beyer and Gishi 1980 1.0*02 cak
X
ID
cak 6.5e-03 cak
ID
Beyer and Gish, 1980 9.7e-02 cak
cak 3.7e-01 cak
l.le-04
ID
ID
ID
5.9e-03
1.7e-02
ED"
3.5e-01
3.2e-04
ID
4.9e-01
ID
6.7e-03
ID
8.2e-01
3.9e-01
cak
Garten and Trabalka, 1983;
Claborn et al., I960;
Claborn et al., 1956 as
. cited in Kenaga, 1980
cak
cak
cak
cak
Clabom eta!., I960;
Claborn et al., 1956 as
cited in Kenaga. 1980;
Garten and Trabalka. 1983
cak
Clabom et alv I960; Travis
and Arms. 1988; Garten
and Trabalka, 1983
cak
Ecosystem
Plants Reference
ZOe-Ql U.S. EPA, 1992e
6.8e-03 U.S. EPA, 1992e
ZOe-Ol Baes et al., 1984
6.0e-02 U.S. EPA. 1992f
l.Se-01 'U.S. EPA, 1992f
XOe-02 U.S. EPA. 1992e
Tle4)2"tJ.S.EPA,;i992e
LOe^Z U7S. EPA, l~992f
1.9e^)3 U.S. EPA, 1992e
l.le-01 U.S. EPA. 1992*
i.4*bi uJS. JEPA; i992f
1.4*02 UiS. EPA; 1992e
7.5*03 Baes et aln 1984
1.9e-02 U^. EPA, 1992e
14*02 US. EPA,1992f
3.9*03 U.S. EPA, 1992e
1.8e-03 U^. EPA. 1992e
• . • . ' (continued)
in
b
W
£
a
M
90
S
-------
Constituent name
Dieldrin
Dielhyl phthalate
Dimethyl phthalate
Endosulfan
Endrin
Fluoranthene
Heptachlor
Heptachlor epoxide
Hexachlarobenzene
Hexachlorocyclohex
ane. gamma- •
(Lindane)
Hexachlorocyclopen
tadiene
Hexachlorophene
Kepone
Lead
Worms
14*02
2.1e-OS
4.1*06
2.9e-04
1.5*02
1.3e-02
9.7e-03
5.3e-03
4.1e+03
5.3*01
7.7*03
2Je+00
3.0*03
1.9*01
Reference
cak
cak
cak
calc
cakj
. ..»..J". :
cak
cak
cak
Belfroid et al, 1994
Clabom. et.al, 1960 as cited in
Kenaga, 1980
cak
cak
cak
Hartenstein et al, 1980; Davies,
1983
Table
5-28 (continued)
Invertebrates Reference
3.3*03
2.6e-06
5.2*07
3.6e-05
1.8e-03
1.7*03
1.2*03
6.6*04
3.7*03
5.8*05
9.6*04
3.1*01
3.7*04
3.2*02
Cooke, 1972 as
cited in WHO.
1989
cak
cak
calc
cak
calc
calc
cak
cak
cak
cak
cak
cak
Beyer et al., 1985;
Beyer and Miller,
1990
Vertebrates
2.3e+01
2.7*06
5.4*07
3.7e-05
1.9e-03
1.8*03
1.3*03
3.5e*00
3.9e-03
6.0*05
1.0*03
3.2*01
3.9e-04
2.7*01
Reference
Mendenhall et al, 1983 as
cited in WHO 1989, Bins,
1978 as cied in WHO,
1989; Aulerich et al.. 1972
as cited in WHO, 1989;
Nebeker et al, 1992;
Clabom et al., 1960
cak
cak
calc
cak
cak
cak
Garten A Trabalka, 1983
cak
cak
cak
cak
cak
Hoffman et al., 1985 as
cited in WHO, 1989;
Osbom et al, 1983 as cited
in WHO. 1989
Plants
2.9*02
0
4.4e-K)
0
3.8*01
.3.8*02
4.i*02
4.9*02
7.0*02
2.6*02
2.9*01
5.6*02
2.0*03
9.7*02
2.4*01
Reference
U.S. EPA,
i
U.S. EPA.
U.S. EPA.
U.S. EPA,
US. EPA;
U^. EPA,
U.S. EPA,
U.S. EPA.
US. EPA,
U.S.EPA,
U.S.EPA,
U.S.EPA.
U.S.EPA.
US. EPA.
1992*
1992e
1992e
1992e
1992e
1992e
1992e
1992e
1992e
1992e
1992e
1992e
1992e
1992f
(continued)
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
-------
Table 5-28 (continued)
Constituent Baae
Mercury
Methoxychki
Methyl parathion
Molybdenum
Nickel
Parathion
Pentachlorobenzene
Penuchlorophenol
Polychlorinated
biphenyls
(Aroclor-1254)
Selenium
Silver
TCDb; 2,3.7.8-
Toxaphene
Trichlorophenoxy-
acea'c acid, 2,4,5-
Vanadium
Zinc
Worms
n>
3.2e-03
6.8e-05
ib
ib
6.1e-04
1.7e-K)3
6.0e+02
1.8e+01
ID
m
fclwOO
9.4e-03
L3e-04
ID
12*+00
Reference
.- •
cak
cak
cak
Belfroid et al. 1994
vjn Gestel and Wei-chun Ma,
• 1988
cak
v
Mariinucci et aL, 1983 as cited
in Walton & Edwards. 1986; Abt
A Associates, 1993; Reinecke A
Nash. 1984
cak
cak
Davies. 1983; Helmke. 1979
Invertebrates Reference
ID
4.0e-04 cak
8.6e-06 cak
ID
ID
7.6e-OS cak
l.Se-03 cak
1.4e-03 cak
2.3e+00 cak
ID
ID ™: '"•
1.3e+00 AbtAAssoc,
1993
1.2e-Q3 cak
1.6e-05 cak
ID
ID
Vertebrates
2-le+OO
4.2e-04
8.9e-06
ID
ID
7.9e-05
6.2e+01
1.7e-01
3.5e+00
ID
"ID'
,
3.7e-01
1.7*05
ID
ID
Reference
Borg et al, 1970 as cited
in WHO. 1989; Finley et
al, 1979 as cited in WHO.
1989; Aulerich et al.. 1974
as cited in WHO. 1989
cak
cak
cak
Garten and Trabalka, 1993
Garten and Trabalka. 1983
Garten A Trabalka. 1983
Koaba et aL. 1978 as cited
in Geyer et aL. 1986;
Jensen et al, 1981 as cited
in Geyer et aL, 1986;
Bowman et aL, 1983 as
cited in Geyer et al, 1986;
Abt A Associates 1993*
Garten A Trabalka, 1983
Garten and f nlbianka,
1983
cak
Plants
2.0e-03
9.3e-02i
8.6e-01
8.5e-01
l.le-01
X4e-01
4.4e-02
4.Se-02
8.9e-03
6.0e-03
Reference
Baes
US.
US.
US.
US.
US.
US.
US.
US.
US.
etal.
EPA,
EPA,
EPA.
EPA.
EPA.
EPA.
EPA,
EPA,
EPA,
4.0e-01 Baes et al.
-
S.Oe-02
6.0e-01
5.5e-03
9.6e-02
US.
US.
US.
us.
us.
EPA,
EPA,
EPA,
EPA,
EPA,
1984
1992e
1992e
1992f
1992f
1992e
1992e
1992e
1992e
1992f
1983
1992e
1992e
1992e
1992f
1992f
ID = Insufficient data. '
5.0 EXPOSURE 5 J Concentrations for Ecological Receptors
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
validated in a limited number of studies and exposure scenarios. Knowledge uncertainty on
exposure inputs such as the distribution of dietary habits (e.g., fish intake) is significant
Empirical data on bioaccumuladon are limited and sometimes difficult to interpret due to
differences in analytical techniques for hydrophobic organics. Moreover, the spatial and
temporal aspects of exposure are difficult to characterize in a generic assessment with respect
to actual field exposures. The exposure scenarios for ecological receptors were developed
under the assumption that wildlife spend most of their lives and/or critical life stages within
the area of contamination and consume contaminated prey and vegetation. Although input
parameters, that describe the physical setting of the exposure were assigned values that are
consistent with the hunting ranges, habitats, etc., of the ecological receptors, the presumption
of exposure to a homogenous contaminated media/food source tends to increase the level of
conservatism. Indeed, the generic nature of the ecosystems does not account for soil or
surface water characteristics (even at a regional level) that could strongly influence the
bioavailability of constituents. Specific issues of concern are discussed below with respect to
the freshwater and terrestrial ecosystems.
53.6.1 Generic Freshwater Ecosystem
5J.6.1.1 BAF Model Selection
The models developed by Thomann (1989) and Thomann et al. (1992) provide
estimates of bioaccumulation for hydrophobic organics. Although the models have been
validated with limited data, their broad application to other freshwater ecosystems has not yet
been demonstrated. The utility of the models is found in their general agreement with
measured data (within approximately a factor of 4) and, especially, the application of a simple
limnetic (or pelagic) food chain and a littoral (sediment-based) food web. However, there are
some inconsistencies between the models: (1) the food web approach appears to be relevant
to the limnetic zone as well as the littoral zone, (2) different lipid fractions and body weights
were used for each model, and (3) the littoral model included invertebrates and the limnetic
model did not The consequences of using different lipid fractions and body weights were
examined with respect to the limnetic food chain. Sensitivity analyses indicated that using
the littoral food web values in the limnetic food chain resulted in a reduction in B APIs by
less than a factor, of 2 for the upper trophic levels. Therefore, the uncertainty introduced by
using different physical characteristics for fish is believed to be small relative to model
uncertainty. Competing models such as the Lake Ontario food web model developed by
Gobas et aL (1993a, b) present altema&se methods to estimating bioaccumulation in the food
web. Unlike the Thomann models, the Gobas model assumes that chemical uptake in
zooplankton is predominantly by water and food consumption is insignificant However, the
Gobas' model requires a starting water concentration as an input and, therefore, does not
generate bioaccumulation factors directly. The model can be used to estimate food chain
multipliers since bioaccumulation is relative to each trophic level (Le., the FCM should not
change regardless of the starting concentration). In addition, preliminary calculations suggest
that FCMs derived using the Gobas model are not appreciably different from those generated
using, the Thomann models.
August 1995 5-206
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
5.3.6.1.2 Measured BAF vs. Modeled BAF x
In the Great Lakes Water Quality Initiative Criteria Documents for the Protection of
Wildlife (U.S. EPA, 1993e) and the supporting technical background document, Derivation of
Proposed Human Health and Wildlife Bioaccumulation Factors for the Great Lakes Initiative
(Stephan, 1993), predicted BAFs are calculated using food chain multipliers based on
Thomann's model (1989). The FCMs were derived using Thomann's limnetic food chain
model and, therefore, predicted BAFs are expected to be similar to those estimated in this
report However, a comparison of FCMs and BAFs from Stephan (1993) and those calculated
as described in Section 5.3,2.1 suggests that the methods are not exactly the same. The
FCMs appear to be calculated as though the chemical assimilation efficiency (E) has been set •
equal to 0.8, the food assimilation efficiency (a). When the model is run using 0.8 as the
chemical assimilation efficiency, the FCM results are identical to those used in the Great
Lakes Inititiative. In addition to this uncertainty in running the model, measured BAFs were
always preferred to BAFs predicted using a BCF value and an FCM. A great deal of
variability has been demonstrated in measured bioconcentration and bioaccumulation data.
Identical BCF tests on die same species may vary over two orders of magnitude, even when
normalized to lipid content Based on the uncertainty in measured values, BAFs predicted -
with the Thomann models were preferred unless the chemical fell into a "special" category as
described above (e.g., TCDD, PAHs, mercury, toxaphene). For these chemicals, a measured
value or default value was selected. It should be noted that, because measured BAFs
typically reflect total water column concentrations, the protective exposure concentrations
estimated using measured BAFs are total water concentrations and not dissolved
concentrations.
5.3.6.1 J Dependence on Key Physical Properties
The sediment criteria model, BCF regression equations, and the Thomann models are
largely driven by log K^ However, log Kow varies widely for more hydrophobic chemicals
(i.e., log Kow > 4.5) due largely to. difficulties in analytical methods. Relatively 'new
analytical techniques such as the slow stirring method have resulted, in new standards for
hydrophobic compounds. For example, the log Kow for di-ethylhexylphthalate (DEHP) .
predicted using structure activity information was approximately 8.5 and values obtained
using the classical shake-flask method were approximately 4.5. De Bruijn et aL (1989)
demonstrated that the log K,,w for DEHP was 7.45 + 0.061 using the slow stirring method.
The authors point out that although th,e^classical shake-flask procedures are adequate for
compounds with log KgW values of less than 4.5, deviations occur for more hydrophobic
compounds because of the formation of octanol emulsions in the shake-flask method.
Therefore, the models are. only as valid as the log K,,w values used as inputs. Although
exaustive data collection efforts have reduced the uncertainty associated with spurious log
Kow values, available data are limited for some groups of chemicals.
August 1995 5-207
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
5J.6.1.4 Dietary Assumptions
The exposure scenario assumes that the diet for all receptors consists either of fish or
invertebrates. This assumption, although conservative, is highly plausible. However, the
eagle is known to consume other larger prey (e.g., herring gull) and may not be adequately
represented as a fifth trophic level consumer of large fish. In addition, the BAF1 derived for
benthic invertebrates was used to estimate exposures to the mallard and lesser scaup because
they were assumed to feed on invertebrates and they consume at least some portion of those
invertebrates from shallow sediments. However, both the mallard and lesser scaup consume a
variety of insects and, depending on the season, approximately one-third of the mallard diet
may consist of aquatic vegetation. Moreover, assuming that the BAF for benthic invertebrates
° istfoughly equivalent to the BAF for all invertebrates consumed by these birds has not been
•validated with empirical BAF data. Nevertheless, it is important to include lower trophic
"level consumers and the invertebrate BAF developed for the littoral zone appears to be a
reasonable approximation for other aquatic invertebrates as welL Assuming that the receptor
dt£ts consist entirely of fish or aquatic invertebrates probably results in a "high-end" exposure
"fdrPthese ecological receptors, although the distribution cannot be quantified without
" bldaccumulation data on insects and bioconcentration data on aquatic plants.
Calculation of TC
03*4-.
Calculating a tissue concentration for prey relies heavily on the assumption that steady-
state equilibrium has been achieved, an assumption that may not be appropriate for all (or
most) freshwater exposure scenarios. Althou^ it is difficult to quantify, the uncertainty
' generated by assuming steady state should not be ignored. Additional uncertainty is
introduced by converting the acceptable tissue concentration from a lipid-based concentration
to a whole-body concentration and back again. However, it appears unlikely that conversion
is a significant source of uncertainty relevant to other sources if: (1) the distribution of lipid
content in fish is assumed to be normal or log-normal, and (2) the variability in lipids
associated with species, season, and physiologic state. The uncertainty in representing other
species of fish and mammals with one lexicological benchmark is likely to be much greater.
5J.&2 Generic Terrestrial .Ecosystem ~
5.3.6 J.I Model Selection
Whereas the assessment of freshwater ecosystems was based on a series of algorithms
that, to a large extent, were driven by K^, the assessment of the terrestrial ecosystem uses a
simple algorithm that requires chemical- and species-specific information on the interaction
between the chemical and the ecological receptor. This creates large knowledge uncertainties
since the algorithm requires BAFs for wildlife categories that currently lack mechanistic
models. In addition, the quantitative relationship between KgW and chemical assimilation
efficiency (a) and bioaccumulation in animals has not been delineated by trophic level in the
generic terrestrial ecosystem. Equations governing uptake, excretion rates, and growth
dilution for terrestrial wildlife are apparently less developed than their aquatic counterparts.
August 1995 5-208
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
As a result, the exposures attributed to terrestrial food webs tends to generate higher risks at
lower trophic levels'. Although the elevated exposure concentrations at lower trophic levels
may represent actual exposures, it seems unlikely that, for all bioaccumulative chemicalsr the
upper trophic levels witt never be more highly exposed than the lower trophic levels. In
short, the model is driven by data availability and constructed contrary to the trophic level
approach used for the freshwater ecosystem.
5.3.6.2.2 Hunting Range Uncertainty
Some receptors (e.g., hawks, kestrels, and foxes) have hunting ranges that are
significantly larger than the contaminated areas assumed for the fate and transport algorithms
(see Section 6). For example, a 200-hectare agricultural field was used to represent the
central tendency for field size and the home range of a red-tailed hawk can vary from a few.
hundred hectares to over 15 hundred hectares. Nevertheless, it was determined that 100
percent of the diet would be assumed to originate from contaminated sources based on
behavioral patterns of the representative species and the nature of the toxicological
benchmarks. The behavior patterns of many representative species suggests that it is
reasonable to assume that, during breeding seasons, these species would stay close to the den
or nest to care for their young (Chapman and Feldhamer, 1982; U.S. EPA, 1993t).
Consequently, the effective, hunting range during sensitive life stages may be significantly
reduced, assuming that prey availability does not drop due to outer stressors. In addition, the
toxicological benchmarks were often derived from subchronic reproductive studies in which
the study animals were administered the chemical during a sensitive life stage (e.g., days 7 to
12 of gestation). The use of reproductive benchmarks from studies of subchronic duration
tends to narrow the temporal window for exposure and, as a result, the relevance of the total
hunting range to exposure becomes less important For example, areas with high prey density
that are also within the area of contamination may provide substantial exposures in the short
(and possibly longer) term. If these exposures occur during sensitive life stages, the
toxicological benchmark may be exceeded. Although assuming that 100 percent of the diet
comes from a contaminated area is conservative, it was considered a reasonable approach to
protecting animals at sensitive life stages (i.e., developmental stages).
5 J.643 Dietary Fractions
The dietary fractions identified in the Wildlife Exposure Factors Handbook (U.S. EPA,
1993g) encompassed a broad range of dietary preferences for most receptors. Major sources
of variability include geographical reg&n, season, test species, and analytical methods (e.g.,
mass of various prey vs. incidence of prey in the gut). Although spring and summer diets
were selected to be consistent with sensitive life stages such as breeding and whelping, the
variability adds significant uncertainty to the terrestrial food web model. In addition, limited
data are available on soil and sediment ingestion in most wildlife species. As with dietary
preferences, the data appear to be highly variable, making it difficult to characterize
appropriate high-end and central tendency distributions. In the absence of available data,
consensus among ecologists and natural scientists may be necessary to develop "typical" diets
for different regions of the country.
August 1995 5-209
-------
5.0 EXPOSURE 53 Concentrations for Ecological Receptors
5J.6.2.4 Food Item Categories *- •
The terrestrial food web algorithm includes food items in four categories: earthworms,
invertebrates (excluding earthworms), vertebrates, and plants. To evaluate food web'
exposures, bioaccumulation (bioconcentration for plants) values are required for appropriate
prey items. Considering the variability in bioaccumulation of fish species, grouping various
prey species under very general categories (e.g., vertebrates) greatly increases the uncertainty
of the estimate. Even assuming that BAFs are available for several species of vertebrate or
insect, selecting a geometric mean may greatly underestimate exposure if die receptor's diet is
skewed toward certain species. Although the robust data set examined for TCDD provides
more confidence in the food item categories, few chemicals have sufficient data to reach even
a moderate level of confidence. Alternative methods should be considered to address the
disparity between the data requirements and data availability for the terrestrial food web. For
example, specific food chains could be identified to represent a complete terrestrial food web.
5.3.6.2.5 Bioaccumulation Factors
As stated above, BAFs (or BCF for plants) were required for each of the food
categories. Bioaccumulation factors for terrestrial prey probably represent the largest source
of uncertainty in estimating acceptable exposure concentrations. For example, empirical data
on bioconcentration in terrestrial plants is nonexistent for the majority of constituents of
concern. Empirical data are available for some metals; however, the database on hydrophobic
organics is woefully inadequate. In fact, the «*-«us of empirical data on plant uptake and
accumulation of organics was recently evaluated for a database on uptake/accumulation,
translocation, adhesion, and biotransformation of chemicals in plants (Nellessen and Fletcher,
1993). This database, referred to as UTAB, is one of the most comprehensive data sources
available on chemical processes in plants and contains over 42,000 records taken from more
than 2,100 published papers. The authors found that, with the exception of pesticides, uptake-
response data for organic chemicals is available for roughly 25 percent of the chemicals
monitored by EPA. As a result, plant BCFs were estimated using equations developed by
(McFaiiane, 1991). Recent modeling developments for plant uptake may offer better
mechanistic tools to estimate uptake of chemicals in a variety of plant species (e.g., Trapp et
al., 1994; Riederer, 1994; Paterson and Mackay, 1995; and Matthies and Behrendt, 1994).
Similarly, the database on bioaccumulation in terrestrial vertebrates is extremely poor and the
default method proposed above introduces considerable uncertainty in the exposure
concentration estimates, likely to rangePfrom one to two orders of magnitude.
5.3.6.2.6 Exposure to Plant Receptors
Although the fate and transport algorithms presented in Section 6 allow for plant uptake
through transpiration, adhesion, and soil-to-root, the phytotoxicity benchmarks were taken
from studies in which the soil-to-root exposure pathway was assumed to be the predominant
portal for plant uptake. Recent work on dioxin reassessment (U.S. EPA, 1992n) suggests that
August 1995 5-210
-------
5.0 EXPOSURE 5J Concentrations for Ecological Receptors
air-to-leaf transfer and adhesion may result in significantly greater exposures to plants than
through the soil. Therefore, the plant lexicological benchmarks may underestimate exposure
to plants from other sources, particularly for semivolatile hydro-phobic chemicals such as
dioxin. :- -
<=*»
August 1995 5-211
------- |